CVIP Laboratory

University of Louisville, KY Phone: (502) 852-7510,

(502) 852-2789, (502) 852-6130 Fax: (502) 852-1580

Email: cvip@cairo.spd.louisville.edu http://www.cvip.louisville.edu

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About CVIP…

Our Vision:

Providing a Better Understanding of the Human and Computer Vision Systems

Our Mission: The Computer Vision and Image Processing (CVIP) Laboratory was established in 1994 at the University of Louisville and is committed to excellence in research and teaching of computer vision and its application. The CVIP has two broad focus areas: computer vision and medical imaging. The laboratory is housed in a modern state-of-the-art research building and is linked, via a high-speed network, to the university’s medical center. The laboratory hosts unique and modern hardware for imaging, computing and visualization. Among the active research projects at the laboratory are the following:

1. Trinocular active vision, which aims at creating accurate 3-D model of indoor environments. This research is leading to creation of the UofL CardEye active vision system, which is our research platform in advanced manufacturing and robotics.

2. Multimodality image fusion, which aims at creating robust target models using multisensory information. 3. Building a functional model of human brain based on integration of structural information (from CT and MRI) and

functional information (from EEG signals and functional-MRI scans). The functional brain model is our platform for our brain research in learning, aging, and dysfunctions.

4. Image-guided minimally invasive endoscopic surgery which aims at creating a system to assist the surgeons locate and visualize, in real-time, the endoscope’s tip and field of view during surgery.

5. Large-scale visualization for modeling and simulations of physical systems, and applications in visual reality. 6. Building a computer vision-based system for reconstruction of human jaw using intra oral video images. This research

will create the UofL Dental Station, which will have various capabilities for dental research and practice. 7. Vision-based system for autonomous vehicle navigation. 8. Image modeling, segmentation, registration and pattern recognition.

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About The University of Louisville The University of Louisville is a state supported urban university located in Kentucky’s largest metropolitan area. It was a municipally supported public institution for many decades prior to joining the university system in 1970. The university has three campuses. The 177-acre Belknap Campus is three miles from downtown Louisville and houses seven of the universities eleven colleges and schools. The Health Science Center is situated in downtown Louisville’s medical complex and houses the university’s health related programs and the University of Louisville Hospital. On 243-acre Shelby Campus located in eastern Jefferson County are the National Crime Prevention Institute and the University Center for Continuing and Professional Education. In recent years, the university has also offered expanded campus courses at Fort Knox, Kentucky. About The J. B. Speed School of Engineering Founded in 1924, the University of Louisville Speed Scientific School (recently re-named The J.B. Speed School of Engineering) is the University’s college of engineering and applied sciences. Endowments from the James Breckinridge Speed Foundation, named after an influential Louisville industrialist (1844-1912), started and have continually supported the Speed School. The School consists of Chemical Engineering, Civil & Environmental Engineering, Computer Engineering and Computer Science, Electrical and Computer Engineering, Industrial Engineering and Mechanical Engineering programs.

Research 2006 1. 3D Reconstruction from Stereo Research Team: Alaa E. Abdel-Hakim Aly, Asem M. Ali, Ham Rara, Aly A. Farag The goal is investigating novel approaches for enhancing stereo matching and hence the overall stereo

reconstruction is improved. Machine stereo vision is inspired from the human vision system. The origin of the word “stereo” is the Greek word “stereos” which means firm or solid, i.e. with stereo vision, the objects are seen solid in 3D. In stereo vision, the same seen is captured using two sensors from two different angles. The captured two images have a lot of similarities and smaller number of differences. In human perception, the brain combines the captured to images together by matching the similarities and integrating the differences to get a 3D model for the seen objects. In machine vision, the 3D model for the captured objects is obtained finding the similarities between the stereo images and using projective geometry to process these matches. The bottleneck of reconstruction using stereo is finding matching correspondences between the stereo pair. In this project, we aim at developing novel approaches to enhance the matching process, especially for human face reconstruction. Approaches Three main approaches are taken in consideration for this project: Graph-cut based matching, sub-pixel correspondence search and scale-space matching. The general framework for correspondence matching using graph cuts is as follows: Let P be the set of pixels in an image and L be a set of labels. A labeling f assigns a label fp 2 L to each pixel p 2 P. The quality of a labeling is given by an energy function:

where N are the edges in the four-connected image grid graph, V (fp, fq) is the cost of assigning labels fp and fq to two neighboring pixels, and Dp(fp) is the cost of assigning label fp to pixel p. For stereo the labels are disparities. The parameters of the energy function are:

) , V (fp, fq) = min (fp − fq , KVDp(fp) = ¸min (Il(x, y) − Ir(x − fp, y), K ) D

Where K , KV D and ¸ are constants. We use the graph cut to find the optimal labeling f, which corresponds to the best disparity map. The weighted undirected graph consists of set of vertices correspond to the image pixels and a set of unordered pairs of neighbor pixels are the edges. Sink and source are two distinguished vertices called terminals. A set, N-links, connect pairs of neighboring vertices with costs V (fp, fq). A set, T-links, connect vertex with terminals with costs Dp(fp). A cut is a set of edges such that terminals are separated in the induced graph. The cost of the cut is the sum of its edges weights, which corresponds to the desired minimum solution of the proposed energy function. Subpixel search and scale-space employs: building scale-space representation using a Gaussian image pyramid. Then, a coarse-to-fine search strategy through the subpixels of the pyramid is used to find the correspondences or feature descriptors are built to find these correspondences. Sample results:

(a) (b)

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(c) (d)

(e) (f)

(g) (h)

Fig 1. Results of disparity generation using different algorithms (a,b) The original stereo pair (c-f) Disparity generated using different graph cuts-based algorithm (g,h) Occluded points (marked in red).

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The goal is developing novel approaches for enhancing the shapes obtained from shading.

Fig 2: Visual correspondences using scale-space subpixel search.

References: [1] A. E. Abdel-Hakim and A. A. Farag. Colored local invariant features for distinct object description in vision-based intelligent systems. In

X. F. Zha, editor, Artificial Intelligence and Integrated Intelligent Information Systems: Emerging Technologies and Applications, chapter 10, pages 178–205. Idea Group Publishing, Hershey, PA, USA, October 2006.

[2] A. E. Abdel-Hakim and A. A. Farag, “CSIFT: A SIFT Descriptor with Color Invariant Characteristics,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR06), (New York, NY), 17-22 June 2006, pp. 1978-1983.

2. Shape from Shading

Research Team: Abdel-rehim Ahmed, Aly A. Farag

SFS is a classic problem in computer vision. It uses the brightness variation in a single image to compute the three dimensional shape of a surface

Approach Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this work, we developed a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the developed modeling gives a unique solution without any information about the height at the singular points of the surface. This work is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.

Sample Results

Fig 3: Results for Venus head. (a) The ground truth. (b) The synthetic image. (c) The reconstructed 3D shape using our approach. (d) The reconstruction error.

Fig 4: Results for a real image of a bear. (a) The bear image. (b) The reconstructed surface using our approach. (c) using Tsai-Shah algorithm (d) using Prados-Faugeros algorithm.

Reference: [1] Abdelrehim Ahmed and Aly Farag, "A New Formulation for Shape from Shading for Non-Lambertian Surfaces," Proc. of IEEE Conference

on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, USA June 17-22, 2006

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[2] M. G. Mostafa, Sameh M. Yamany and Aly A. Farag, “Integrating Shape From Shading and Range Data,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’99), Fort Collins, Colorado, pp. 15-20, June 1999.

[3] M. G. Mostafa, S. M. Yamany and Aly A. Farag, ``Integrating Stereo and Shape from Shading,'' IEEE International Conference on Image Processing (ICIP'99), Kobe, Japan, Vol. 3, pp. 130-134, October, 1999.

[4] M. G. Mostafa, S. M. Yamany and Aly A. Farag, ``Data fusion for 3D object reconstruction,'' Proceedings of SPIE, vol. 3523, p.8899, Sensor Fusion and Decentralized Control in Robotic Systems, Paul S. Schenker; Gerard T. McKee; Eds., Oct 1998.

3. Autononmous Navigation: PDE-Based Robust Robotic Navigation

Research Team: Mohamed Sabry, Alaa El-Din Aly, Aly A. Farag The goals of this project are: 1) developing novel path planning approaches that overcome the voids in the

current approaches; and 2) integrating the current resources at CVIP lab in mobile robots, simulation, and active vision, in order to develop a complete navigation platform that can be used in different missions for various environments.

This project concentrates on path planning for robotic navigation. The main goals of this project are the following: 1) developing novel path planning approaches that overcome the voids in the current approaches; 2) integrating the current resources at University of Louisville CVIP lab in mobile robots, simulation, and active vision, in order to develop a complete navigation platform that can be used in different missions for various environments. This project is envisioned to be the first step in a potentially long-term collaboration with NASA in smart robotics R&D for space exploration, especially unmanned missions to outer space.

The proposed method works as follows: for global planning approach, a medial point of the map is selected automatically to be a point source that transmits two wave fronts of different speeds. The first front propagates with a moderate speed to capture the map topology, while the second one propagates much faster at medial points such that the safest paths intersect the propagating fronts at those points of maximum positive curvature, which are identified by solving an ordinary differential equation (ODE). The motion of the front is governed by a nonlinear partial differential equation (PDE), which is efficiently solved using robust level set methods. On the other hand, for local path planning, a fast wave front is propagated from the starting point until the target is reached and then the path is identified between them by solving again the same ODE. The speed model of the front is function of some features of the map such as minimum distance to boundary for planar environments or minimum height map for terrain ones. The speed model is controlled by a single parameter, which controls the path extraction procedure to be either the safest, shortest, or hybrid.

In this project, we propose a general and robust robotic path planning for both planar and terrain environments to generate shortest, safest, and hybrid paths using a partial differential equation-based technique. For planar environment, the proposed method can handle both global and local planning. In global planning, first, we compute all possible safe paths of the map and then select the one that satisfies the optimal safest path between a starting and target locations. 1. This work proposed a general, robust, and fast path planning framework for robotic navigation by solving a special first-order, nonlinear partial differential equation, known as the Eikonal equation, using level set methods. 2. A level set speed function is derived such that the minimum cost path between the starting and target locations in the environment is the optimum planned path. 3. The speed function is controlled by one parameter alpha, which takes one of three possible values to generate the safest, the shortest, or the hybrid planned path. The hybrid path is much safer than the shortest path, but less safe than the safest path. 4. The main idea of the approach is to propagate a monotonic wave front with a particular speed function from a starting location until the target is reached and then extract the optimum planned path between them by solving an ordinary differential equation (ODE) using an efficient numerical scheme. 5. The motion of the front is governed by the Eikonal equation, which is efficiently solved using an accurate numerical solution method, called the multi-stencils fast marching method (MSFM). The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum path for partial or complete configuration space. The robustness of the proposed framework is demonstrated by correctly extracting planned paths of complex maps with several obstacles.

Accomplishments: In robotic navigation, path planning is aimed at getting the optimum collision-free path between a starting and target locations. The optimality criterion depends on the surrounding environment and the running conditions. In this work optimum path planning problem is related to the variational minimum-cost path problem that finds the path

that minimizes the cumulative travel cost from point to some destination 2),0[:)( ℜ→∞sγ

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A x in . If the cost u is only a function of the location

2ℜx x in the given domain, then the cost function is called isotropic, and the minimum cumulative cost at

is defined as,

(1)

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Where is the minimum cost required to travel from to)(xT A x . The path that gives minimum integral is the minimum cost path. In geometrical optics, it has been proven that the solution of Eq. (1) satisfies the Eikonal equation, which describes the propagation of a monotonic wave.

(2)

The wave speed is the reciprocal of the cost function . In this work, we have derived a new level-set speed function such that the minimum cost path between two locations in the map is the optimum planned path. The speed function is given by,

)(xF )(xu

(3)

α αwhere is the Euclidean distance field and )(xD is a positive real number. We have derived an analytical expression for as follows,

(4)

The grid spacing in x and directions are given by y yΔxΔ and , respectively.

α plays an important role in generating the optimum path as follows, We have shown that, the parameter

⎪⎩

⎪⎨

⎧

>

==

c

c

CenteredHybridShortest

PathOptimumαααα

αpp00

(5)

Several accomplishments have been achieved in this work, which can be enumerated as follow: 1. Authored the medial-path extraction theorem, which is concerned with deriving a new level-set speed function such that

the minimum cost path between two locations in the map in the optimum planned path. 2. Derived an analytical expression for determining the value of the parameter alpha that controls the type of the optimum

planned path (shortest, safe, or hybrid). 3. Proposed a novel numerical solution method, called the multi-stencils fast marching method (MSFM), for solving the

Eikonal equation, which is a highly accurate and fast. 4. Reduced the computational complexity of the framework to O(N), where N is the number of grid points. 5. Handled efficiently local path planning with obstacles. 6. Shared of ideas and results with NASA Collaborators.

We have developed a method called level-set graph (LSG), which converts any map into a graph (i.e., set of clusters), from which we can determine the topological nodes of the map. In Fig. 5, we show the LSG of several maps of different complexity. In global planning, if the robot is doing a regular job that requires the extraction of different safest paths in the map, it is better to extract the entire safest paths of that map (skeleton) and store them for latter use. In Fig. 6, we show the entire safe paths of different maps with different complexity.

Fig. 5: The proposed level set graph can accurately capture the topology of the map. The clusters X, M, and S denote extreme, merging, and successor clusters, respectively.

Fig.6: The entire safest paths of several maps for global planning. It is useful to store all of them if the robot is doing a regular job within a known environment. Now, given any two points in the map, a method has been developed to extract the safest path between them based on the pre-computed skeletons as shown in Fig. 7.

Fig. 7: Global planning to find the safest path between S and E using the pre-computed skeletons of the map. In local planning, the robot only knows some information about the environment, normally the area in its close vicinity. A method has been developed to extract the optimum planned path within this area. As the robot moves, the optimum path segments are concatenated to form the final optimum planned path as shown in Fig. 8.

(a) (b) (c) Fig. 8: Local planning between S and E: (a) shortest path. (b) hybrid path. (c) safest path.

In Fig. 9, we show the safest path between two locations in the map. While the starting location varies its position, the target location is fixed.

Fig. 9: The computed safest paths of the same map at different starting locations but with the same target

location.

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In Fig. 10, we show the shortest, hybrid, and safest paths by the proposed global planning technique. It is clear that the hybrid path is much safer than the shortest path but less safe than the safest path.

(a) (b) (c)

Fig. 10: Global planning between two locations of two different maps: (a) shortest path. (b) hybrid path. (c) safest path. In Fig. 11, we show the shortest and safest paths that are computed by the proposed local path planning method.

(a) (b)

Fig. 11: Local planning between two locations: (a) shortest path. (b) safest path. In Fig. 12, we show the shortest and the minimum energy (safest) paths between two locations on a terrain surface given its height map.

(a) (b) Fig. 12: The shortest and safest paths of a terrain environment are superimposed on both the (a) height map and (b) iso-contours.

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4 Biometrics: Human Identification Using Biometrics Research Team: Alaa E. Abdel-Hakim Aly, Abdelrehim Ahmed, Asem Ali, Sergey Chekmenev, Ham Rara, Mike Miller

and Aly A. Farag

The goal of this three year program is development of a system for remote acquisition of a person’s three-dimensional shape and for identification of the person using that shape information.

Biometrics is the study of automatic methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. Examples of physical characteristics include fingerprints, eye retinas and irises, and hand measurements. The focus of this project is in face patterns, specifically 3D shape from various sensors. CVIP Lab Setups: 3D Reconstruction Setup and CardEye The reconstruction setup consists of (a) Cyberware 3030 laser scanner, which acquires 3D shape and texture, (b) Sony Donpisha XC-003 CCD camera to capture a sequence of images, and (c) workstation loaded with CVIP algorithms for 3D reconstruction of the captured images. The scanner setup can be used to build a database of 3D faces and capture sequence of images for each subject and utilize these images for recognition based on both 2D and 3D information.

Fig 13: 3D Reconstruction Setup (Cyberware 3030 scanner, Sony CCD camera, Linux workstation)

The CardEye is a robot-controlled, trinocular multi-sensor, active vision system that has three pan-tilt-zoom (PTZ) cameras in its vision module. The original goal of the setup was for general object 3D reconstruction but it is to be used for face-specific biometrics.

Fig 14: CardEye Setup

Long-Distance 3D Stereo/2D Face Recognition Setup The setup consists of two Canon EOS Digital Rebel XT cameras, fixed with Canon EF-S (18-55mm) lens. The system can act as a 2D face recognition system if one camera is used and a 3D face recognition system if the two cameras (stereo pair) are employed. Currently, algorithms for laboratory-controlled conditions are being perfected. The long-distance aspect of this setup is being verified by using appropriate telephoto lenses.

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Fig 15: Long-Distance 3D Stereo/2D Face Recognition Setup Preliminary Results

Fig 16 CVIP Lab 3D face database samples

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Fig 17: Sample faces as point clouds and surfaces ready for recognition

5. Vital Signs from Thermo Imaging

The idea behind the measurements of the arterial pulse using thermal Infra Red (IR) camera resides in the capability of the camera to capture the heat variations on the skin caused by the arterial pulse propagation. In spit of the simplicity of this idea there are many non-trivial research questions to be solved to permit the reliable measurements. The pulsating heat contribution produced by a superficial artery is of a very low magnitude in comparison to other noisy or pulse irrelevant heat patterns observed on the skin. Arteries typically located deeper inside the tissue and do not produce sharp thermal imprints. This makes accurate detection of the Region of arterial pulse Measurement (ROM) and its tracking in thermal video not an easy task to achieve. The anatomy of the carotid artery and the Superficial Temporal Artery (STA) is illustrated on the left of the figure below. The carotid complex and STA branches are the most accessible superficial arteries for the thermal IR based measurements. The experimental setup is illustrated on the top center of the figure and the thermal of these regions of interest is underneath.

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Experimental Setup

Anatomy Sample result

Thermal Imagery

Fig 18: Non-Contact and Non-Intrusive Vital Signs measurements at the CVIP Laboratory Our model for measurement of the arterial pulse from the STA using passive thermal IR sensors has physical and physiological basis and as such is of fundamental nature. The IR sensor is used to capture the heat pattern from superficial arteries and a blood vessel model is used to describe the pulsatile nature of the blood flow. A multresolution wavelet-based signal analysis approach is used to extract the arterial pulse waveform, which lends itself to various physiological measurements. We validate the results using traditional contact vital sign monitor as a ground truth. Eight people of different age, race and gender have been successfully tested. Sample result is illustrated on the right of the figure above. The resultant arterial pulse waveform exactly matches the ground truth readings. The essence of our approach is the automatic detection of ROM, from which the arterial pulse waveform is extracted. Our approach is novel and has never been reported in the literature. From pattern recognition prospective, this research will have major consequences in healthcare, surveillance, intent detection, education and various non-intrusive applications. From a computer vision prospective, this research will affect the design characteristics of image capturing sensors used in various video-based applications.

6. Image Guided Intervention in Eye Surgery Research Team: Hossam Hassan, Aly A Farag, Henry J. Kaplan,Tongalp H. TEZEL The goal of our project is to develop minimally invasive image-guided surgical procedures for the retina (i.e. vitreoretinal surgery) that will initially enhance, and subsequently replace, current methods of vitreoretinal surgery. Our initial approach will involve the development of software to create a 3D real-time model of the eye that can be used by the retinal surgeon employing conventional techniques of vitreoretinal surgery. We envision eventually coupling this image-guided intervention with robotic technology to replace the current conventional approach to vitreoretinal surgery. We believe that image-guided vitreoretinal procedures have the potential to improve patient care by enabling faster procedures, minimizing unintended damage to healthy tissue, decreasing the incidence of medical errors producing fewer complications and allowing surgical intervention at a distance. Our early work in this field is aimed at merging the current tools in with a 3D OCT overlay as envisioned in figure 19 below.

Figure 19: Lay out of an eye surgery with the microscope and cutting tools. This application will integrate an OCT to the Operating Microscope setup in order to transfer the status-quo into image-guided surgery, which will eventually be robotics-driven as well.

Research design and methods In order to alter the status quo significantly, an image-guided approach we are pursuing includes the following: 1) Real-time visualization of the ROI in terms of depth and spatial support through the multimodality image fusion (e.g., OCT, SLO, US, and stereo vision); 2) Creation of the pressure pattern induced by the surgical tool by real-time recording of the pressure induced by the surgical tool and the location of its tip – this will create a free-form cloud of points in 3D space as well as pressure at each point; 3) Study of the physical characteristics of the proliferative tissue , by collecting samples of this tissue during actual surgery, in order to assess its elasticity; 4) Creating an adaptive feedback mechanism linking the visualization system and the surgeon in order to learn the surgeon’s approach for removal of the proliferative tissue – this will eventually derive a control mechanism for an image-guided robotic system for proliferative diabetic retinopathy. 7. Automatic Detection of Renal Rejection after Kidney Transplantation

Research Team: Ayman El-Baz, Seniha Esen Yuksel, Rachid Fahmi, Aly A Farag, Mohamed A. Ghoneim, Taek A. El-Diasty Our goal is to automatically detect renal rejection after kidney transplantation using Dynamic Contrast

Enhanced Magnetic Resonance Imaging (DCE-MRI). Currently, the diagnosis of renal rejection is done via biopsy, which subjects the patients to risks like bleeding and infections and it can lead to over or underestimation of the inflammation. Use of MR and Ultrasonography images will replace these risky procedures and give quantifiable results about the kidney for early detection of rejection.

Renal transplantation is a well-accepted method in the treatment of end-stage renal diseases. In the United States, approximately 12000 renal transplants are performed annually and acute rejection is the most important cause of graft failure after renal transplantation. However, diagnosis of acute transplant dysfunction remains a difficult clinical problem. Currently, the diagnosis of rejection is done via biopsy, but biopsy has the downside effect of subjecting patients to risks like bleeding and infections. Moreover, the relatively small needle biopsies may lead to over or underestimation of the extent of inflammation in the entire graft. Therefore, a noninvasive and repeatable technique is not only helpful but also needed in diagnosis of acute renal rejection.

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We are trying to use Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) imaging technique to detect the size increase and the abnormal flow patterns that develop non-uniformly throughout the whole kidney. Methods: Our image analysis approach consists of three major steps as shown in Fig. 20:

• Segmentation of DCE-MRI scans in order to extract the kidney; • Motion compensation using our newly developed non rigid registration method which based on scale spaces and active

contours theories; • Generation of the kidney functionality curve during the DMRI scanning cycle; • Generation of the daily progress metric based on color Doppler US.

Fig. 20: The complete system to follow the progress of kidney transplantation using either DCE-MRI. Results: Our current efforts are concentrated on the use of the DCE-MRI technology which is known to be a powerful imaging technique since it provides both functional and anatomical information and it overcomes the temporal resolution problems with conventional MRI by special encoding techniques. However, in DCE-MRI, the abdomen is scanned repeatedly and rapidly after the injection of the contrast agent; therefore, (i) the spatial resolution of the dynamic MR images is low, (ii) the images suffer from the motion induced by the breathing patient which necessitates advanced registration techniques, (iii) the intensity of the kidney changes non-uniformly as the contrast agent perfuse into the cortex which complicates the segmentation procedures. In our research, we are trying to find automated techniques (i) to segment the kidney, (ii) to correct for the motion artifacts, (iii) to get kidney functionality curve, and (iv) to detect the rejection by comparing the kidney functionality curves for rejected and non-rejected kidneys. Our current approaches can be summarized into 4 steps:

1) Finding a shape model for the kidney using sign distance functions and gray level probability densities, 2) Using this shape model in deformable models to segment the kidney. Three of the original images and their

segmentation results are shown in Fig 21. 3) Application of our new non-rigid registration using iso-contours to register every image onto the first image of the

original sequence. Our registration results are shown in Fig 22. 4) Finite Element-Based Validation Technique of the registration process. 5) Forming the kidney functionality curve by tracing the gray level changes at particular locations in the kidney as shown

in Fig.23.

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Fig 21: A typical DCE-MRI sequence of a kidney and its segmentation results

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The major objective of this study is to exploit the reported anatomical abnormalities in certain regions of the autistic brain relative to the typically developing brain, in order to devise robust and accurate classification approaches between the two groups. Our up-to-date results are based on the structural MRI analysis of the white matter and the corpus callosum since the findings with respect to these two parts of the brain are more consistent compared to other brain regions. The developed techniques, which are based on geometrical models and shape analysis, were tested on postmortem and in-vivo data sets. The corresponding results are very impressive and, unlike traditional techniques, our approaches are insensitive to age and volume effects

0 4 8

1.8

121

1.2

1.4

1.6

DMRI sequence

Sig

nal m

easu

red

from

DM

RI

Source Image Target Image Registration Results

Fig. 22: Results of non rigid registration of DMRI sequence shown in Fig. 2 with the first image of the sequence.

Fig. 23: Average signal measured in the cortex tissues after non rigid registration.

F.E meshes before & after deformation

Displacement fieldThe average registration error is about 0.96mm, with a maximum of 1.07mm, a minimum of 0.02mm, and a standard deviation of 0.55.

Fig. 24: FE-Based Validation Of the used non-rigid registration technique. A 2D bio-mechanical model of the kidney is built

and deformed using the FE method. The original and the simulated deformed images are registered using our technique. The recovered displacement field is compared to the bio-mechanically simulated one for validation purposed. The obtained results with an error analysis are reported in this figure.

Fig. 24 gives the pattern of signals collected from the cortex. Rejected kidneys develop delayed perfusion patterns compared to normal kidneys because of the graft enlargement. Our future work will concentrate on combining ultrasound and DCE-MRI measurements and on detecting the changes in these patterns automatically. 8. Neuroimaging-Based Classification Techniques of Autistic vs. Typically Developing Brain Research Team: Rachid Fahmi, Ayman El-Baz, Manuel Casanova, Hossam Hassan and Aly Farag Autism is a complex developmental disability that typically appears during the first three years of life and is the result of a neurological disorder that affects the normal functioning of the brain, impacting development in the areas of social interaction and communication skills. Both children and adults with autism typically show difficulties in verbal and non-verbal communications, social interactions, and leisure or play activities. Autism is a spectrum disorder and it affects each individual differently and at varying degrees, with milder forms like Asperger syndrome, in which intellectual ability is high but social interaction is low, and with the most severe cases typified by unusual, self-injurious, and aggressive behaviors. Such behaviors may persist throughout life and may inflict a heavy burden on those who interact with autistic persons. Cognitive impairments may also last over time and often result in mental retardation in autism sufferers. According to the Centers for Disease Control and Prevention (C.D.C.), about 1 in 166 American children fall somewhere in the autistic spectrum. We aim at taking advantage of the abnormalities of some brain regions in order to devise new techniques that allow the classification of autistic subjects vs. typically developing ones by analyzing their respective brain MR images.

Focus on analyzing the white matter (WM) and the corpus callosum (CC). my of the WM in autistic brains

(e.g., asymmetry, volume, fractional anisotropy, etc). Increasing agreement from structural imaging studies on the abnormal anato

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The deficits in the size of the corpus callosum and its sub-regions in patients with autism relative to controls iwell established.

s

Two types of MRI data are used: Post-mortem Proton density MRI Pre-mortem T1- weighted MRI Data set size: 256x256x124 Data Resolution: 0.9375x0.9375x1.5

The Data at hand unde . These steps include, among others: • Skull Stripp : he in-vivo data sets to remove the noise artifacts represented by

ormed as a preprocessing step.

Classification Approaches:

rgo a series of preprocessing stepsing A skull stripping method is applied to t

the non brain tissue such as eyes, skin and fats. Background removal will also be perf• Segmentation: Segmentation of both the postmortem as well as the in-vivo MRI’s is performed to extract the white

matter, gray matter, and the Cerebro-Spinal Fluid (CSF). The level sets segmentation approach, which is a home developed approach, will be used in this work.

Fig. Preprocessing of the Postmortem data sets

Fig 25: Preprocessing of the In-Vivo data sets

s on thAs mentioned earlier, we focu e analysis of the WM and the CC. White Matter Analysis:

ote from the following figure the difference in folds and gyrifications inN the white matter between the two groups. It is this of the WM between the two classes that motivates our research. difference in the anatomy

Example of Segmentation into WM, GM, and CSF

S ta ck o f Im ag e s

2 D S lice E xtra c te d W M (R e d )/G M (B lu e )

B in a ry R e p re se n ta tion o f

W M

W hole 3 D H e ad M o d e l W M V isu a liza tio n

Original slice Smoothed kull StrippeS d

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Fig26. Different Views of the 3D WM of autistic and healthy brains. The zooms show the

difference in folds of the WM between the two groups.

Autistic Control

Approach:

We compute the distance map inside of the segmented WM volume

Cumulative distributions functions of the distance map inside the WM of a training set from each group are computed, normalized, and averaged.

For a given subject: • Compute and normalize the DM inside its WM. • Compute corresponding CDF and compare it to the two averaged CDF’s using the Levy distance.

Fig 27: Approach Show in the frames above

2) Corpus Callosum Analysis:

Only the pre-mortem data is considered. Corpus callosum is manually segmented from the segmented brain date sets. Illustration of the proposed non-rigid registration approach.

Fig 28 : Illustration of the proposed non-rigid registration approach.

For each group:

Choose a reference within a training set. Register the rest to the reference. Compute CDF of the corresponding deformation fields. Average the deformation fields.

Fig 29: Average the deformation fields. The following figure illustrates how a given subject is being classified by registering its CC to the two reference CC’s representing each class. We compute the CDF’s of the two recovered displacement fields we compare them to the averaged displacement fields representing each class using the Levy distance. The smallest distance indicates the class to which the testes subject belongs.

Fig 30: Subject is classified by registering its CC to the two reference CC’s representing each class.

18

9. Fly-Over: A New Visualization Technique for Virtual Colonoscopy 9. Fly-Over: A New Visualization Technique for Virtual Colonoscopy Research Team: M. Sabry Hossouna, Dongqing Chen, and Aly A Farag Research Team: M. Sabry Hossouna, Dongqing Chen, and Aly A Farag

G

oal: Our goal is to develop and clinically validate a new visualization technique for irtual colonoscopy, which is called virtual fly-over. irtual colonoscopy, which is called virtual fly-over. v

The fly-over approach is an integration of computer vision, computational geometry, and computer graphics techniques. The colon is segmented from CT data and 3D model is created. A variational framework is proposed to compute the medial axis of the

The fly-over approach is an integration of computer vision, computational geometry, and computer graphics techniques. The colon is segmented from CT data and 3D model is created. A variational framework is proposed to compute the medial axis of the colon, which serves as the datum for splitting the colon’s

ouflaged by the complicated colon topology) as shown in Figure 31. Our goal is four folds: (1) Adapt and quantify the fly-

colon, which serves as the datum for splitting the colon’s

ouflaged by the complicated colon topology) as shown in Figure 31. Our goal is four folds: (1) Adapt and quantify the fly-

surface longitudinally into exactly two halves. A virtual camera is assigned to each half to perform fly-over navigation (e.g., visualize the colon surface area in its field of view without being

surface longitudinally into exactly two halves. A virtual camera is assigned to each half to perform fly-over navigation (e.g., visualize the colon surface area in its field of view without being

19

cam camover approach for colonoscopy; (2) Validate the approach vs. optical colonoscopy on a significant number of patients and a well-defined protocol; (3) concurrently test the fly-over approach against competing approaches in VC; and (4) Visualize colonic polyps with a different color (e.g., color coding) to facilitate the screening process by the radiologist

over approach for colonoscopy; (2) Validate the approach vs. optical colonoscopy on a significant number of patients and a well-defined protocol; (3) concurrently test the fly-over approach against competing approaches in VC; and (4) Visualize colonic polyps with a different color (e.g., color coding) to facilitate the screening process by the radiologist

Fig. 31: Illustration of the proposed fly-over system. Initially,

Fig. 32: Comparison of surface visibility coverage for 3 colon datasets A, B, and C by (a-c) fly-

the colon’s medial axis is computed, which is used to split the colon into two halves. Finally, each half is assigned a virtual camera to perform fly-over navigation.

through in antegrade direction. (d-f) fly-through in both antegrade and retrograde directions. (g-i) The proposed fly-over navigation in antegrade direction.

Validation: We have quantitatively validated the proposed fly-over navigation method by computing the percentage of the colon surface that could be visualized during navigation. To demonstrate the effectiveness of the proposed system, we compare it with the traditional fly-through navigation in terms of surface visibility coverage. The comparison has been performed on clinical colon datasets acquired using Siemens Sensation CT scanner. The dataset volume is 512 512 580× × with voxel size 30 74 0 74 0 75mm. × . × .

gNavi ation Navigation Surface Visibility (%)

Direction Technique Dataset A taset C Dataset B Da

Fly-through 79.29 78.95 79.38 Antegrade

Propo er 99.35 99.68 99.33 sed Fly-ov

Fly-through 77.89 75.74 79.00 Retrograde

Propo er 99.6 99.91 99.68 sed Fly-ov

Fly-through 93.64 93.12 93.38 Antegrade + Retrograde

Propo er 99.7 99.95 99.73 sed Fly-ov

20

Goal: Our goal is to develop a computer aided diagnosis (CAD) system for automatic colonic polyp detection, false positive reduction and color coding to highlight the detected polyps.

esults:

0. Geomet c Features Based Computer Aided Diagnosis System for Virtual

Dongqing Chen, and Aly A Farag

urvature-based geometric features have been proven to be important for colonic polyp detection. In this project, we present an

e novelties can be summarized as: (1) we accurately estimate the maximum and minimum principal curvatures to overcome three common problems associated with some regular methods; (2) based on the

Table 1: Comparison of Surface Visibility by the Proposed Fly-over and the Traditional Fly-

Fig. 33. During fly-over navigation, although the polyp of the first colon half (c) has

through R appeared during the antegrade fly-through navigation, the polyp of the second colon half (d)

has been overlooked during both the antegrade (a) and retrograde (b) fly-through navigation.

1 ri Colonoscopy

Research Team: Cautomatic detection framework and a new color coding scheme to highlight the detected polyps. Th

obtained principal curvatures, more false positives are reduced; and (3) a new color coding scheme is designed to highlight the detected polyps. The key idea is to place the detected polyps at the same locations in a newly created polygonal dataset with exactly the same topology and geometry properties as the triangulated mesh surface of real colon dataset, and assign different colors to the two separated datasets to highlight the polyp

21

s. Finally, we validate

e

ile sphere-like polyps with different sizes of 8mm, 10mm and 12mm are generated as shown Figure 34(a). And Figure 34(b) shows their locations.

fferent locations of the simulated colon as shown in Figure 34(d), and

l d visualized by assigning red color as shown in ig.35. Since the phantoms are relatively simple, the false positives are easily removed by the accurately calculated principal

, 12mm, 15mm and 20mm, are created and inserted as shown in Figure 36(a).

Fig 36. Results of the complicated colon phantom.

Fig. 34: (a) Three sphere-like polyps with different sizes, (b) inserted at different locations, (c) two sphere-like and one ellipselike polyps, and (d) inserted at different locations

Fig. 35 : Detection and color coding results of the colon phantom in Fig. 1. The polyps and colonic wall

a),

the proposed geometric features-based polyp detection framework and color coding scheme by computer simulated and real colon datasets. For sixteen synthetic polyps with different shapes and different sizes, the sensitivity is 100%, and false positive is 0. For four real colon datasets, the proposed algorithm has achieved the sensitivity of 75% at thaverage false positive of 6 per case. Validation: Experiment I, three sessin

In experiment II, two sphere-like polyps with sizes of 10mm and 15mm, and one ellipselike polyp with size of 12mm are rated as shown in Figure 34(c). They are inserted at digene

they are placed on the different sides in order to increase the complicity of the simulated phantom. A l the polyps as shown in Figure 34(b) and Figure 34(d), are totally detected anFcurvatures and false positive reduction solution.

In experiment III, five thick spherical folds with diameters ranging from 15mm to 60mm are simulated in a colonic lumen. Five sessile sphere-like polyps with sizes of 7mm, 9mmAll the polyps as shown in Figure 36(a) are totally detected as shown in Figure 36(b) to Figure.36(f). are visualized by red and grey color, respectively. (

(b) and (c) corresponding to the three polyps as shownin Fig. 1(b), and (d), (e) and (f) corresponding to the three polyps as shown in Fig.1(d)

R

22

esults: We use a clinical colon dataset to validate the proposed framework. We select five boundary points as the centers, here the five polyps with efficient sizes of 7:5mm, 9mm, 12mm, 15mm, and 18mm are placed. The third synthetic polyp has the

polyps, polyp 1, polyp

Figure 37. Five synthetic polyps inserted in real colon dataset. (a) detected polyps, (b) their positions, (c) detected by mean curvature and colored by the lookup approach, and (d) detected by the proposed

wshape of ellipsoid. All the positions of these polyps are clearly indicated by the arrows at the 3D real colon object as shown in the column (b) of Fig. 37. Among the five2 and polyp 4 are inserted on the folds, while the other two are placed in the concave areas between two folds.

detection framework and colored by the lookup table approach

1

23

Our goal is to create a 3D prototype of the jaw impression. The current research is directed towards improving the data acquisition system and using finite element models for the 3D reconstruction of the jaw.

he overall aim of this research es beyond the traditional approaches in

1. Jaw Modeling Project

ag, Abdelrehim Ahmed, Hossam Hassan and Mike Miller

is to apply model-based computer vision methodology to develop a system for dentistry that o diagnosis, treatment planning, surgical simulation and prosthetic replacements. This

lete Volumetric 3D Model of the Human Jaw The purpose of this work is to int of the human jaw. Our previous

ork developed an accurate model of the human jaw surface. Unfortunately this surface is not enough for the analysis purpose

Research Team: Aly A Far Tgcomputer vision methodology should obsolete the use and avoid the discomfort of the mold process used today as well as substantially improve the data accuracy available to the oral surgeon and dental care personnel. Our main objectives are as follows: (i) to design a flexible data acquisition system to capture high quality calibrated intra-oral images of the jaw, teeth, and gums (ii) to develop methods for accurate 3-D reconstruction from the acquired sequence of intra-oral images using state-of-the-art approaches in 3D reconstruction, including shape from shading, and space carving techniques; (iii) to develop and automate a robust algorithm for fusion of data acquired with accurate and fast 3D data registration; (iv) to develop a specific object segmentation and recognition system to separate and recognize individual 3D tooth information for further analysis and simulations; (v) to develop algorithms for the simulation of tooth movement based on the finite element method and deformable model approaches; (vi) to develop a database software with a user friendly GUI interface which stores the acquired digital models in a database that can be retrieved on demand and shared over computer networks for further assistance in diagnosis and treatment planning; and (vii) to enable the use of haptic feedback interfaces on our 3D jaw models in future work.

Fig 38: A Comp

roduce a detailed approach for constructing a complete modelwwhich requires a detailed model of the teeth and their roots. A database of volumetric 3D models of teeth is constructed. The upper part of the tooth coming from the shape from shading algorithm provided in our previous work is matched with one of the database teeth. A non-rigid registration technique is used to deform the matched tooth with the given partial-model. Combining this information with the jaw details, we have now a complete volumetric 3D model of the human jaw. This model is suitable for the analysis process which will include Finite Element work to analyze the stress and strains of different simulations. The simulation processes will include tooth-implanting and alignment.

Fig 39: Registration steps of different 3D models

24

Goal: Combined nearly 2000 CT images to create a construct a virtual skull, and translated this data in to a solid 3-D model of clear epoxy resin.

Elastic Registration of Tags and Roots

D model of clear epoxy resin.

Elastic Registration of Tags and Roots

his research introduces a new method for shape registration by matching vector distance functions. The vector distance function

representation is more flexible than the c t enables us to better control the shapes T

onventional signed distance map since iregistration process by using more general transformations. Based on this model, a variational frame work is proposed for the global and local registration of shapes which does not need any point correspondences. The optimization criterion can handle efficiently the estimation of the global registration parameters. A closed form solution is provided to handle an incremental free form deformation model for covering the local deformations. This is an advantage over the gradient descent optimization which is biased towards the initialization and more time consuming. Results of real shapes registration will be demonstrated to show the efficiency of the proposed approach with small and large global/local deformations.

Example #1 Example #2

Fig ure 40: To Tooth Deformation Example #2: A source red contour is deforming towards the green target.

2. Images and 3D Reconstruction of an Ancient Human Skull

oth Deformation Example #1: A source red contour is deforming towards the green target.

1Research Team: Charles Sites, Aly A Farag

t’s Valley of th Queens. One of 8 o f the same family found in the Burial grounds of 42 individuals. Fig 41. The ancient mummy of Tchaenhotep, an Egyptianfrom Egyp

25

ackground: In August of 2006 the CVIP Lab combined nearly 2000 CT images to create a construct a virtual skull, and then

evisit the “Streets of Cairo” exhibAnthropology Hall at Washingtwas Louisvillian Samual Thrustowas later responsible for bringing thto Kentucky. The mummy became aattraction at the museum in the lowthe Louisville Free Public Library,and York Street.

During the major Ohio River Floo

Fig 42: To the left is the solid 3-D model of clear epoxy resin. the caved head of the coffin.

to fall on the mummy and, somehow the head became separated o restore and repair the waterlogged mummy and coffin. The

spark the imagination of area visitors at several locations. The

questions poised about the mummy and the coffin. Where was the coffin from? What was

uring the embalming process, Egyptians used a combination of resin, beeswax,

e consisted of 512x512 pixels (picture elements), so

ue) and the bone tissue is white. We set a range where any pixel above a value of 255 is

are more than 25 million X,Y,Z ngles which make

Btranslated this data in to a solid 3-D model of clear epoxy resin. The stereography process took 52 hours. “This Egyptian, Tchaenhotep, has come a long way from an ancient burial site. The tombs were first discovered in 1903, Over one hundred

years ago, The mummy and coffin came to the United States to be displayed at the 1904 St.

thousands to ition and the

on University n Ballard. He

ese artifacts compelling er level of

at Fourth

d of 1937, the

Louis World’s Fair. Among th

Skull. Above

mummy was nearly destroyed. Rising waters caused a piano many challenges tfrom the body. The museum curator faced

ummy survived the flood and continued tomexhibit was later moved to Fifth and York Streets and, in 1977, arrived at the present location of West Main Street. The mummy and coffin were on continuous display here until early in this century when they came to rest in the collections storage areas of the Louisville Science Center.

On June 15, 2004 a team of investigators came together to explore the secrets of the mummy and coffin using state of the art medical imaging, forensics, and the latest in restoration technology. year-long investigation was led by Conservator Mimi Leveque to help answer the manyinside the wrappings? Was the body that of a male or female? How old was the mummy at the time of death? How did the mummy die? What was the mummy’s name?

The first stop, Louisville’s Baptist Hospital East, where the team prepared the mummy for x-ray and CAT scan. Considering it’s age, the x-rays revealed the mummy to be in excellent shape. The investigators took a close look at the human tissue and were amazed to find it remarkably well preserved. Dand fat to ensure tissue preservation. Cecilia Yappert, Professor of Chemistry at the University of Louisville, studied some of the tissue and resin, stating, “they knew exactly what to mix, …exactly how to get rid of water, how to make very impermeable barriers, and they also knew the preservation aspects. They knew what antibacterial molecules…that would effectively preserve the tissue.” Researchers determine in addition to the mummy’s name to be Tchaenhotep,. one of 8 of the same family found in the burial grounds of 42 individuals. The place of origin an was found to be Egypt’s famous Valley of th Queens. Tchaenhotep is believe to be female though specific data from the pelvic area could not be examine close enough to complete confirm. Tchaenhotep is believed to had been 25 to 35 when she died. [1]

Technical work: A computer tomography (CT) scan of 198 slices was performed on the mummmy’s head and a data base of images was supplied on CDs in DICOM image format. DICOM data contains important data for 3D modeling such as the slice thickness, orientation, greylevels and measurements of pixel size. Each imagour volume becomes 512x512x198 voxels (volume elements). Each pixel in the image slice was represented by a 16 bit grayscale value giving a graylevel range from 0 (completely black) to 65535 (completely white) By reading the images into a computer and stacking them, a 3D volume of the CT scans can be made virtually inside the computers memory. We used a program called the Visualization Tool Kit (VTK), used extensively in the scientific community visualizing all types of scientific data, including medical image data.

First we read the model into the computers memory where the images are stacked to create a virtual 3D CT scan. The next stage is the process of segmentation where we assign labels to each voxel that declares if the voxel is bone tissue or air. In the CT image air is black (or has a zero vallabeled bone. Once labeled bone or air, we fit a cube to the voxel labeled bone tissue. The cube is sliced diagonally, horizontally, vertically, or not at all based on how the neighboring voxels are labeled. This cube (sliced or not) is then converted to small pyramids made up of triangles and their X,Y,Z coordinates are recorded. In the VTK program we call this the MARCHING CUBES algorithm. The surface generated is called an ISO-surface and the value used to select bone, or air is called the ISO-Value. The ISO-surface is where the boundry between bone and air is defined.

The number of X,Y,Z coordinates creating the ISO-surface is quite large. Our volume consists of 512x512x198 or 51,904,512 voxels and each voxel has an X,Y,Z coordinate. If half of the volume is labeled bone, then therecoordinates. We reduce this by using a process called DECIMATION. Decimation reduces the number of tria

26

Goal: Enhancement of DSA output .

ience Center Mummy Video 2006.- Cortiana Productions

esearch Team: Rachid Fahmi, Asem, Ali, Ayman El-Baz, Aly A Farag, Jasjit Suri

Descripti

up the ISO-Surface by removing duplicate or un-needed triangles. Once the triangles are reduced, two more actions a performed. First we create surface for the computer to display by filling the triangles in a process we call polydata mapping. And second, we create a stereo lithography (STL) file for use by the rapid prototype machine which creates the 3D plastic model. References: [1] Louisville Sc 13. Image Quality /Denoising Project R

on: Bolus and Mask images are used to cn implemented. The first step reduces the noi

onstruct a DSA image. A two-step approach to enhance the DSA image has ee se by regularization of the gray level value at each pixel with respect to its eighbors. The neighborhood size and topology can be adjusted by the user. The second step enhances the output using a

bnclassification approach in which the parameter estimation is performed by a linear optimization approach. The approach is fully automatic and all parameters are estimated locally; no prior training is needed. Implementation on a single CPU PC machine yields decent results within one second. Another DSA approach is also introduced in which the DSA output was generated by an averaging process of the subtraction of the masks and bolus (or vice versa). The same two-step approach is also implemented on the output of the new DSA algorithm. We show the results of the two-step approach on the DSA output provided by a reference output and the CVIP Lab approach.

Methods:

e following block diagram illustraTh tes the two-step approach to enhance the DSA output.

• Algorithm I

•

Bolus

Algorithm II

ere, the averagings of the masks and the boluses are performed differently. First, the intensities are shifted and scaled for each res, as this pseudo

ode illustrates

Diagram 1: block diagram illustrates the two-step approach to enhance the DSA output. Hframe separately before they are averaged. Effectively, the intensities in each image are converted into z-scoc Preprocessing step: for each frame

find mean m of all pixels in image all pixels in image find SD s of

CVIP EnhancementEnhanced Output

CVIP LAB

DSAMask

Bolus

Mask

( )∑∑= =

−M

i

N

jji mb

NM 1 1

1 Enhanced Output CVIP Enhancement

CVIP LAB

for each pixel in each image for each pixel in each image

27

Goal: Combine Pattern Recognition Algorithms, Natural Language Processing, and data mi esdesign a new facet based search engine enabling the ability to pre-analyze the data on page ability to pre-analyze the data on page

ning techniqu to Web s.

set i = ( i - m ) / s sity to the pixel

end for end for After thi we average the values at each pixel of the pre-processed images, then we normalize between 0-255. Finally,

the CVIP DSA approach to the averaged mask and bolus.

1- Estimate the pdf. for blood vessels and background

Web s.

set i = ( i - m ) / s sity to the pixel

end for end for After thi we average the values at each pixel of the pre-processed images, then we normalize between 0-255. Finally,

the CVIP DSA approach to the averaged mask and bolus.

1- Estimate the pdf. for blood vessels and background

get pixel intensity i get pixel intensity i

assign this new intenassign this new inten

s is done,s is done,we applywe apply CVIP Enhancement Pseudo-code: Input: Image CVIP Enhancement Pseudo-code: Input: Image

2- For each pixel 2- For each pixel

a. Step 1: Denoising:a. Step 1: Denoising: ∑{minargˆ }∈

− −+xNr

prssr

pqqs xxbx λσ

ent:

−=s

sxs yx

b. Step 2: Contrast Enhancem f of blood vessels by 1

ii. Shift right pdf of background by 1 Output: Enh e

sults

i. Shift left pd

anc d Image Re

DSA_CVIP DSA Reference

put

In

Output

eration 5 It1 sec

Output

eration10 It2 sec

Fig 43: Resulting DSA images

esearch Team: Ravi Krishna Mulpuri, Isaiah Sharp, Zhiyong Zhang, Dr. Farag, Dr. Olfa

tudy o search enables users navigate a multi-dimensional information space by combining text search with a progressive narrowing of choices in each

become the prevailing user interaction mechanism in e-commerce sites and is being extended to deal with

14. Pattern and Data Recognition Search Engine

RNasraoui, Mike Miller

Background:

f prior art has bread familiarity with Navigational Search, Direct Search and the Faceted. Faceted Stodimension. It has

28

Goal: To design and manufacture the Kentucky Satellite (KySat-1) as the first educational Pico satellite e of Kentucky. The specific goal of this internship is to

design the main infra structure of the satellite and go as far as possible in implementing its different modules.

me

engine. Infocious (http://search.infocious.com/

menting its different modules.

me

engine. Infocious (http://search.infocious.com/

semi-structured data, continuous dimensions, and folksonomies (tags). Unlike vertical, enterprise, clustering, or visual search engines, the proposed method employs pattern recognition, natural language processing and data mining algorithms to overcomany technical challenges, which are unresolved by current approaches. It is in the faceted search that our emphasis lies. To date the application of this type of search efforts showcase just a fraction of the capability offered by the design of a true general purpose faceted search

semi-structured data, continuous dimensions, and folksonomies (tags). Unlike vertical, enterprise, clustering, or visual search engines, the proposed method employs pattern recognition, natural language processing and data mining algorithms to overcomany technical challenges, which are unresolved by current approaches. It is in the faceted search that our emphasis lies. To date the application of this type of search efforts showcase just a fraction of the capability offered by the design of a true general purpose faceted search

(CubeSat, Fig. 1) launched and operated by the stat

)

rch

language processing and data mining algorithms to define/identify certain aspects of a document, that have previo ly been difficult or impossible to determine, across a large and diverse Web collection.

h , 2005.

.C.: Faceted classification: a guide to construction and use of special schemes, London: Aslib, 1960

5. 2006 Internships with NASA esearch Team Member : Alaa E. Abdel-Hakim Aly

ASA•

presents the categories that the current search results fall into. In their implementation methods [1], Natural Language Processing techniques are used to enhance the Web search. Prior to querying, pages are classified into different categories to allow users to narrow their searches. This engine employs DMOZ directory for training their classifier. The Infocious approach is helpful because it gives users categorical breakdown. The Flamenco approach is extremely helpful for paving the road toward our proposed system. It allows investors to see faceted search operational for various different uses. However, Flamenco is not a general search engine, but rather a program that can be installed to organize specific sets of data, and was not intended to seathe entire Web.

This work will attempt to demonstrate the feasibility of a true General Purpose Multi-Facet Search Engine: Applying pattern recognition, natural

us

References: [1]Alexandros Ntoulas , Gerald Chao and Junghoo Cho, “The Infocious Web Search Engine: Improving Web Searching througLingui ic Analysis” In Proceedings of the World Wide Web (WWW) Conference, Chiba, Japanst[2] Vickery, B[3]J. English, M. Hearst, R. Sinha, K. Swearingen, and K.-P. Yee. “Flexible Search and Navigation using Faceted Metadata”, Submitted for publication, 2002. 1R

Internship: 6/11/2006 to 8/19/200

WhereN

: NASA-Ames, Mountain View CA and Stanford University Stanford CA. • Who: Seven students and two faculty members from five universities in the state of Kentucky. • Organization Kentucky Science & Technology Corporation (KSTC) • Partners: Space and Systems Development Laboratory (SSDL)

Pumpkin Incorporation

Our Stu peration and interfacing Sofware and Payload. dent, Alaa E. Abdel-Hakim Aly, worked specifically in theGround O

Fig 44. CubeSat: A Pico satellite (10x10x10 cm) which is typically used for educational purposes.

Fig 45: The KySat-1 team in the press conference which was held on May 25th, 2006 – Frankfort, KY.

Specific Effort our Team Member participated in. (a) Develop the core SW components for the ground station operation The work in this assignment was to design a software system that is used for ground station operation, which is pointed to by the

the ground software system that receives the transmitted packets from it it over the internet to the data server.

igure 46. KySat: t the design area in the project that I have been working in to implement single-person assigned tasks, as shown in section (4.1.1). The green circles highlight the work area that I have been working in collaboration with other people, section (4.1.2).

red ellipses in Fig.46. In other words, to design and testthe satellite through the antenna and reformats these packets to retransm

UHF Capabilities Transfer capabilities over S-Band

29

F Operation modes (top) basic mode (bottom) advanced mode. The red ellipses highligh

Student/TeacherMorehead State University

S-band

UHF/VHF

backdoor

- Audio from Satellite(a,b) VHF Capabilities - Send DTMF Commands for Audio Transmission and Picture Sequence(b)

KySat

Internet

Basic Mode(a): Morehead controls transmission times/picture taken.Advanced Mode(b): Student has to establish uplink to get audio/command picture to be taken.

- Telemetry Data from Satellite - Pictures - Data for Mode 1 Operation

Ground Ops

UHF

VHF

User

Morehead

S-band

VHF uplink (2m)UHF downlink (70cm)

AX.25 packets

KySat

Internet-Ftp://-Http://-.xml

Information flow from SAT to Morehead only

files

Webpage-On orbit Users Manual-Pass information

User

Labtop

Labtop

TNC

TNC

Strongest signal prevails

30

he purpose of this software is to establish the essential components for the ground station to communicate with KySat and the ata central server. This is achieved by developing a program that can be used to read a packet from a serial port (i.e. the TNC

lnet protocol. acketizing the received packets on the data server

(b) Deve st framework for the communication with the ground station

Tdoutput) and redirect it using a telnet protocol over the internet to the data central server. The essential components for this software involves several modules for doing the following:

• Communication with the TNC output through a serial port. • Appending the necessary headers to the received packets. • Communication with the central server using the RFD 854 te• Retrieving the original data (the sent files) by dep

side.

lop a te

e ground station by simulating the communication process as follows:

gned to receive the input packets from a virtual serial port and retransmit them to the data central server after appending a specific header.

• erver. The use of this server enabled us to debug the sent packets. The ommunication to the server using our KySat ground software has been tested by me with a team member who is

• mat, the appended header and the data server are easy to modify for the flight module (FM) final version.

(c) Paylo

This work developed a test framework for the communication software with th

• A program was desi

“first.aprs.net” was used as an initial test scresponsible to the communication subsystem.

All the settings of the serial port, the packet for

ad Software

ps, the main payload of KySat is the camera system. The goal of this work is to provide simple image ocessing tools to be used on the FM. The plan was to develop a histogram builder for the captured image to be sent as a very

era

isclo 06044 titled: A Framework for Computing Three-Dimensional Curve Skeletons of volumetric objects.

k for Automatic Detection of Lung Nodules in Low Dose Chest CT Scans.

these technologies has proceeded.

sued: US Patent: 3-D Digital Reconstruction of an oral Cavity from a sequence of 2-D Images

According to the ConOprfast clue of the image content. However, I have developed a package that contains many other tools to be used in case of deciding to extend the functionality of the FM image processing modules to other tools beyond the image histograms. For example, the developed package includes image filtering tools, negation beside the histogram builder. The design of the payload softare was primarily directed by discussion with the Stanford KatySat team member who is responsible to the camsoftware.

2006 Patents: Pending or Issued D sure Filed/ Pending: ** 06048 titled: A Framewor* 06047 titled: Multiscale, Non-Contact Measurement of Artereial Pulse Using Thermal Imaging. 06046 titled: Inferring Missing Information Using Dental Databases. * 07002 titled: Virtual Fly-Over: A New Visualization Technique For Virtual Colonoscopy

* University Patent Office : Filing of provisional patent applications on

Is

31

1 Aly A. Farag, Ayman El-Baz, Georgy L. Gimel'farb, “Precise segmentation of multimodal images,” IEEE Transactions on Image Processing Vol. 15, no. 4, April 2006, pp. 952-968.

.

Abstract: We propose new techniques for unsupervised segmentation of multimodal grayscale images such that each region of interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels. We follow the

2.

most conventional approaches in that initial images and desired maps of regions are described by a joint Markov–Gibbs random field (MGRF) model of independent image signals and interdependent region labels. However, our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. We modify an expectation–maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation with which the modified EM algorithm should start. The proposed technique identifies individual LCG models in a mixed empirical distribution, including the number of positive and negative Gaussians. Initial segmentation based on the LCG models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments show that the developed techniques segment different types of complex multimodal medical images more accurately than other known algorithms. M. Sabry. Hassouna, A.A. Farag, S. Hushek, and T. Moriarty, "Cerebrovascular Segmentation from TOF Using Stochastic Models," Medical Image Analysis, Vol.10, no. 1, pp. 2-16, Feb. 2006. Abstract: In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or

the ng

l d.

m

sets

3. ed ed Intelligent Information Systems: Emerging

background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizesintensity distribution of the data, while the high level process characterizes their statistical dependence among neighborivoxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initiaestimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provideTo improve the quality of segmentation achieved by the proposed low level model especially in the regions of significant vascular signal loss, the high level process is modeled as a Markov random field (MRF). Since MRF is sensitive to edges and the intracranial vessels represent roughly 5% of the intracranial volume, 2D MRF will destroy most of the small and mediuvessels. Therefore, to reduce this limitation, we employed 3D MRF, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE), which converges to the true likelihood under large lattice. Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF dataacquired from two different MRI scanners. Experimental results showed that the proposed model provides good quality of segmentation and is capable of delineating vessels down to 3 voxel diameters. A. E. Abdel-Hakim and A. A. Farag. “Colored local invariant features for distinct object description in vision-basintelligent systems.” In X. F. Zha, editor, Artificial Intelligence and IntegratTechnologies and Applications, chapter 10, pages 178–205. Idea Group Publishing, Hershey, PA, USA, October 2006. Abstract: This chapter addresses the problem of combining color and geometric invariants for object description by proposing a novel colored invariant local feature descriptor. The proposed approach uses scale-space theory to detect the most

4. anta, GA, USA, October 8-11, 2006, pp. 337 - 340.

geometrically robust features in a physical-based color invariant space. Building a geometrical invariant feature descriptor in a color invariant space grants the built descriptor the stability to both geometric and color variations. The comparison between the proposed colored local invariant features and gray-based local invariant features with respect to stability and distinction supports the potential of the proposed approach. The proposed approach is applicable in any vision-based intelligent system that requires object recognition/retrieval. At the end of this chapter, we present a case study of a local features-based camera planning platform for smart vision systems. H. E. Abd El Munim and A.A. Farag, “A Variational Approach for Shapes Registration using Vector Maps,” Proc. of IEEE International Conference on Image Processing (ICIP’06), AtlAbstract: The main focus of this paper is the shape representation and registration using vector level set functions. This powerful representation is more flexible than conventional signed distance level sets since it enables us to control the shapes

CVIP Lab Papers Abstracts of 2006

registration process by using more complicated transforms. Based on this model, a variational frame work is proposed for the rigid and non-rigid registration of shapes which can be extended to the higher dimensional case in a straightforward manner and also does not need any point correspondences. The optimization criterion presented can handle efficiently both the rigid and the non-rigid operations together. Experimental results in 2D for real shapes registration are discussed to show the efficiency of the approach with small and large global deformations of shapes.

32

5. , Atlanta, GA, 8-11 October 2006, pp. 341-

A. A. Farag, G. Gimelfarb, A. Elbaz, and A. E. Abdel-Hakim, “Image Alignment Using Learning Prior Appearance Model,” Proc. of IEEE International Conference on Image Processing (ICIP’06)344. Abstract: A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity

6. IEEE International Conference on Image Processing (ICIP’06), Atlanta, GA, 8-11

to the prototype is measured by a Gibbs energy of signal cooccurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms. A. El-Baz, A. A. Farag, G. Gimel’farb, M. A. El-Ghar, and T. Eldiasty, “Fast Unsupervised Segmentation of 3D Magnetic Resonance Angiography,” Proc. of October 2006, pp. 93-96. Abstract: A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal

7. ctober 8-11, 2006, pp. 1029 – 1032.

cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with real data sets confirm the high accuracy of the proposed approach. R. Mohamed, A. Ahmed, A. Eid and A. A. Farag, “Support Vector Machines for Camera Calibration Problem,” Proc. of IEEE International Conference on Image Processing (ICIP'06), Atlanta, GA, USA, OAbstract: This paper presents a statistical learning-based solution to the camera calibration problem in which the Support Vector Machines (SVM) are used for the estimation of the projection matrix elements. The projection matrix is obtained

8. ference on Medical

explicitly by using a dot product kernel in the formulation of the SVM algorithm. The Mean Field Theory is used to approximate an efficient learning procedure for the SVM algorithm. In order to assess the robustness of the proposed approach against noise, the experiments using synthetic data are carried out at different noise levels. The proposed approach is evaluated also with real 3D reconstruction experiments. The experimental results illustrate that the proposed calibration approach is efficient and more robust against noise than other known approaches for camera calibration. A. A. Farag, A. El-Baz, G. L. Gimel'farb, R. Falk, M. Abou El-Ghar, T. Eldiasty, S. Elshazly, “Appearance Models for Robust Segmentation of Pulmonary Nodules in 3D LDCT Chest Images,” Proc. of International ConImage Computing and Computer-Assisted Intervention (MICCAI'06), Copenhagen, Denmark, October 1-6, 2006, pp. 662-670. Abstract. To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules

d

st

9. oc. of International Conference on Medical Image Computing and

are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction analytically identified from a set of training nodules. Appearance of the nodules and their background in a current multi-modal chest image is also representewith a marginal probability distribution of voxel intensities. The nodule appearance model is isolated from the mixed distribution using its close approximation with a linear combination of discrete Gaussians. Experiments with real LDCT cheimages confirm high accuracy of the proposed approach. A. El-Baz, R. Fahmi, S. Esen Yuksel, A. A. Farag, W. Miller, M. Abou El-Ghar, T. Eldiasty, “A New CAD System for the Evaluation of Kidney Diseases Using DCE-MRI.,” PrComputer-Assisted Intervention (MICCAI'06), Copenhagen, Denmark, October 1-6, 2006, pp. 446-453. Abstract: Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper, we introduce a new approach for the automatic classification

10. ntervention

halves. Then, it assigns a virtual camera to ds.

Finally, because the orientation of the splitting surface is controllable, the navigation

of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures by evolving a deformable model based on two density functions; the first function describes the distribution of the gray level inside and outside the kidney region and the second function describes the prior shape of the kidney. In the second step, a new nonrigid registration approach is employed to account for the motion of the kidney due to patient breathing. To validate our registration approach, we use a simulation of deformations based on biomechanical modelling of the kidney tissue using the finite element method (F.E.M.). Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction. M. Sabry Hassouna, A.A. Farag, and Robert Falk, "Virtual Fly-Over: A New Visualization Technique For Virtual Colonoscopy," Proc. of International Conference on Medical Image Computing and Computer-Assisted I(MICCAI’06), Copenhagen, Denmark, October 1-6, 2006, vol. I, pp. 381-388. Abstract. In this paper, we propose a new visualization technique for virtual colonoscopy (VC). The proposed method is called Virtual Fly-Over, which splits the entire colon anatomy into exactly two each half to perform fly-over navigation, which has several advantages over both traditional fly-through and related methoFirst, by controlling the elevation of the camera, there is no restriction on its field of view (FOV) angle (e.g., > 90o) to maximize visualized surface areas, and hence no perspective distortion. Second, the camera viewing volume is perpendicular to each colon half, so potential polyps that are hidden behind haustral folds are easily found.

33

the average surface visibility

11. bbs on

n, Denmark, October 1-6, 2006, pp. 734-741.

can be repeated at a different split orientation to overcome the problem of having a polyp that is divided between the two halves of the colon. Quantitative experimental results on 15 clinical datasets have shown that coverage is 99.59±0.2%. A. El-Baz, A. Ali, A. A. Farag, G. L. Gimel'farb, “A Novel Approach for Image Alignment Using a Markov-GiAppearance Model,” Proc. of International Conference on Medical Image Computing and Computer-Assisted Interventi(MICCAI'06), CopenhageAbstract. A new approach to align an image of a medical object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modelled with a new Markov-Gibbs random field with pairwise interaction model. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs

r

12.

penhagen, Denmark, October 1-6, 2006, pp. 799-806.

derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects bettethan popular conventional algorithms. A. El-Baz, A. A. Farag, G. L. Gimel'farb, M. Abou El-Ghar, T. Eldiasty, “A New Adaptive Probabilistic Model of Blood Vessels for Segmenting MRA Images,” Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06), CoAbstract. A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data.

13. .

Y, Aug-Sep, 2006, pp.

Experiments with synthetic and 50 real data sets confirm the high accuracy of the proposed approach. R. Fahmi, A. Abdel-Hakim Aly, A. El-Baz, and A. Farag, "New Deformable Registration Technique Using Scale Space and Curve Evolution Theory and a Finite Element Based Validation Framework," Procof the 28th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, New York, N3041-3044. Abstract: In this paper, we present a novel and fast approach for nonrigid registration. New feature descriptors are built as voxel signatures using scale space theory. These descriptors are used to capture the global motion of the imaged object. Local deformations are handled through an evolution process of equispaced closed curves/surfaces (iso-contours/surfaces) which

14. Gibbs st 20–

are generated using the fast marching level sets and are matched using the built feature descriptors. The performance of the proposed approach is validated using the finite element method. Both 2D and 3D tissue deformations cases are simulated, and the registration accuracy is quantified by co-registering the deformed images with the original ones and comparing the recovered mesh point displacements with the simulated ones. The evaluation results show the potential of the proposed approach in handling local deformation better than conventional approaches. A. El-Baz, A. Farag, and G. Gimel’farb, and A. E. Abdel-Hakim, “Robust Image Registration Based on Markov-Appearance Model,” Proc. of IEEE International Conference on Pattern Recognition (ICPR’06), Hong Kong, Augu24, 2006, pp. 1204 - 1207. Abstract: A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived

15.

automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms. A. El-Baz, A. A. Farag, G. L. Gimel'farb, R. Falk, M. Abou El-Ghar, T. Eldiasty, “A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans,” Proc. of IEEE International Conference on Pattern Recognition (ICPR’06), Hong Kong, August 20–24, 2006, pp. 611-614.

Abstract: To accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of the visual appearance of small 2D and large 3D pulmonary nodules are jointly used to control the evolution of the deformable model. The appearance prior is modeled with a translation and rotation

16.

825.

invariant Markov-Gibbs random field of voxel intensities with pairwise interaction. The model is analytically identified from a set of training nodule images with normalized intensity ranges. Both the nodules and their background in each current multi-modal chest image are also modeled with a linear combination of discrete Gaussians that closely approximate the empirical marginal probability distribution of voxel intensities. Experiments with real LDCT chest images confirm the high accuracy of the proposed approach. A. El-Baz, A. A. Farag, R. Fahmi, S. Esen Yuksel, M. Abou El-Ghar, T. Eldiasty, “Image Analysis of Renal DCE MRI for the Detection of Acute Renal Rejection,” Proc. of IEEE International Conference on Pattern Recognition (ICPR’06), Hong Kong, August 20–24, 2006, pp. 822-

Abstract: Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, a novel nonrigid-registration algorithm is employed to account for the motion of the kidney due to patient breathing, and finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results.

34

17.

7-920.

A. El-Baz, A. A. Farag, G. L. Gimel'farb, M. Abou El-Ghar, T. Eldiasty, “Probabilistic Modeling of Blood Vessels for Segmenting MRA Images,” Proc. of IEEE International Conference on Pattern Recognition (ICPR’06), Hong Kong, August 20–24, 2006, pp. 91

Abstract: A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar blood flow (normal subjects) to turbulent flow in abnormal cases like anemia or stenosis. It results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data.

18.

06, pp. 65-73.

Experiments with both a phantom and 50 real data sets confirm the high accuracy of the proposed approach. A. El-Baz, A. A. Farag, G. L. Gimel'farb, and A. E. Abdel-Hakim, “Experiments on Robust Image Registration Using a Markov-Gibbs Appearance Model,” Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR’06) and Statistical Techniques in Pattern Recognition (SPR’06), Hong Kong, China, August 17-19, 20

Abstract: A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our approach results in more robust registration than the search for the maximal mutual information or similarity of scale-invariant local features. Dongqing Chen, M. Sabry Hassouna, A.A. Farag, and Robert Falk, "An Improved 2D Colonic Polyp Segmentation Framework Based on Gradient Vector Flow Deformable Model," Proc. of International Conference on Medical Imaging and Virtual Reality (MIAR'06), August 20-24, 2006, Shang

19.

hai, China, pp. 373-380 Abstract: Computed tomography colonography (CTC) has been proved to be a valid technique for detecting and screening colorectal cancers. In this paper, we present a framework for colonic polyp detection and segmentation. Firstly, we propose to use four different geometric features for colonic polyp detection, which include shape index, curvedness, sphericity ratio and

20.

the absolute value of inner product of maximum principal curvature and gradient vector flow. Then, we use the bias-corrected fuzzy c-mean algorithm and gradient vector flow based deformable model for colonic polyp segmentation. Finally, we measure the overlap between the manual segmentation and the algorithm segmentation to test the accuracy of our frame work. The quantitative experiment results have shown that the average overlap is 85.17% ± 3.67%. M. Sabry Hassouna and A.A. Farag, "Accurate Tracking of Monotonically Advancing Fronts," Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06), New York, NY, USA June 17-22, 2006, vol I, pp. 355 - 362. Abstract: A wide range of computer vision applications such as distance field computation, shape from shading, and shape

ing

ed

ains. The new method is called the multi-stencils fast marching (MSFM),

ls and then picks the solution that satisfies the fast marching causality

over its 26-neighbors. For those stencils that are not aligned with the rder

21. EEE .

if their color contents are ignored. This paper addresses this problem and proposes a novel colored local

22.

representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solvsuch equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improvversion of the FMM that is both highly accurate and computationally efficient for Cartesian domwhich computes the solution at each grid point by solving the Eikonal equation along several stencirelationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils coverthe 8-neighbors of x, while in 3D space, 6 stencils cnatural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher ofinite difference scheme. A. E. Abdel-Hakim and A. A. Farag, “CSIFT: A SIFT Descriptor with Color Invariant Characteristics,” Proc. of IInternational Conference on Computer Vision and Pattern Recognition (CVPR’06), New York, NY, 17-22 June 2006, pp1978-1983. Abstract: SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassifiedinvariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach. A. Ahmed and A. A. Farag, “A New Formulation for Shape from Shading for Non-Lambertian Surfaces,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, USA June 17-22, 2006. Abstract: Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate

ffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS approximation of the diproblem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are

35

23. on in and Beyond the

presented to show the efficiency of the proposed method. To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS. Sergey Chekmenev and Aly Farag, “Multiresolution Approach for Non-Contact Measurements of Arterial Pulse using Thermal Imaging,” Proc. of 3rd IEEE International Workshop on Object Tracking and ClassificatiVisible Spectrum (OTCBVS'06), New York, NY, USA, June 22, 2006. Abstract: This paper presents a novel, multiresolution (MR) approach for non-contact passive measurements of the artepulse along the arterial tree using thermal imaging of the most acces

rial sible regions of human face and neck. The method

24. Image Analysis (MMBIA’06), New

automatically detects Region/s Of Measurement (ROM) of the arterial pulse using the periodic nature of arterial thermal patterns. This ROM constitutes a skin area where the heat pulsation produced by an artery is most pronounced. The output of the algorithm is a descriptive arterial pulse waveform. To the best of our knowledge, our results are novel and have not been reported before. Some potential applications of this work are in the field of non-contact health state/physiology monitoring, magnetic resonance imaging, smart rooms, polygraph testing, and intent identification. A. A. Farag, A. El-baz, H. E. Abd El Munim, and S. E. Yuksel, “Level Set Segmentation using Statistical Shape Priors,” Proc. of IEEE Computer Society Workshop on Mathematical Methods in BiomedicalYork, NY, USA, June 17-18, 2006, pp. 78-85. Abstract: A novel and robust 3-D segmentation approach is proposed using level sets based on shape constraints. The approach depends on both the gray level and shape information. A partial differential equation (PDE) is developed to control

25. ternational Workshop of Computer Vision

the evolution of the level sets. The PDE does not include weighting coefficients to be tuned, overcoming the disadvantages of other PDE approaches. The shape information is gathered from a set of the signed distance maps representing the training data as a histogram of the occurrences of the points inside and outside the object. We use a novel statistical approach to get a probability density function (pdf) for the signed distance map of the points inside and outside and also the distribution of gray level inside and outside the object. The proposed statistical approach is based on modeling the empirical density function (normalized histogram of occurrence) for either the gray level distribution or signed distance map with a linear combination of Gaussians (LCG) with positive and negative components. We modify an Expectation-Maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation for the modified EM algorithm to start with. The pdf’s of the signed distance and intensity gray level are embedded in the speed function of the level set specifying the direction of evolution. Experimental results show how the approach is accurate in segmenting different types of 2-D and 3-D data sets including medical images. Ayman El-Baz, Aly A. Farag, Asem Ali, Georgy L. Gimel'farb, Manuel Casanova, “A Framework for Unsupervised Segmentation of Multi-modal Medical Images,” Proc. of the Second InApproaches to Medical Image Analysis(CVAMIA’06), Graz, Austria, May 2006, pp. 120-131. Abstract: We propose new techniques for unsupervised segmentation of multi-modal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of gray levels. We

26. dical Imaging, ISBI’06, Arlington, Virginia, April 2006, pp.

follow most conventional approaches such that initial images and desired maps of regions are described by a joint Markov–Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. Initial segmentation based on the LCG-models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments with medical images show that the proposed segmentation is more accurate than other known alternatives. N. El-Zehiry, M. Casanova, H. Hassan, and A. A. Farag, “Effect of Mnincolumnar Disturbance on Dyslexic Brains: An MRI Study,” Proceedings of International Symposuim on Biome1336-1339. Abstract:The minicolumn is generally considered the basic unit of the neocortex in all the mammalian brains. Enlargement of the cortical surface is believed to occur through the addition of minicolumns rather than a single neuron.This study aims at

testing the hypothesis that brain developmental disorders can be diagnosed and analyzed in terms of the minicolumnar disturbance. To do this, we propose to correlate the pathological findings in terms of the minicolumnar structure to the MRI findings in terms of volumetric analysis.

Acknowledgments

36

The CVIP Lab Acknowledges the following funding sources:

• Norton Healthcare System • Foundation •

stitute m

earch

State University (M.S. in Biomedical Engineering), University of Michigan (M.S. in Bioengineering), and Purdue

lPhi Kappa Phi. Dr. Fa

Chuck Sites is a University of Louisville staff member for the Electrical and Computer Engineering Department. He received a Bachelor degree in Physics from the University of Louisville in 1990. He has

ike Miller is a University of Louisville Staff member for the CVIP Lab. He received his Masters Degree

• The US Army National Science • National Cancer In

Jewish Hospital Foundation• The Whitaker Foundation

• Kentucky Lung Cancer Progra • Silicon Graphics Incorporated • Office of Naval Research • Air Force Office of Scientific Res

Staff

Aly A. Farag was educated at Cairo University (B.S. in Electrical Engineering), Ohio

University (Ph.D. in Electrical Engineering). Dr. Farag joined the University of Louisville in August 1990, where he is currently a Professor of Electrical and Computer Engineering. His research interests are concentrated in the fields of Computer Vision and Medical Imaging. Dr. Farag is the founder and director of the Computer Vision and Image Processing Laboratory (CVIP Lab) at the University of Louisville, which supports a group of over 20 graduate students and postdocs. Dr. Farag's contribution has been mainly in the areas of active vision system design, volume registration, segmentation and visualization, where he has authored or co-authored over 80 technical articles in leading journals and international meetings in the fields of computer vision and medical imaging. Dr. Farag is an associate editor of IEEE Transactions on Image Processing. He is a regular reviewer for a number of technical journals and to uding the NSF and the NIH. He is a Senior Member of the IEEE and SME, and a member of Sigma Xi and rag was awarded a "University Scholar" in 2002.

national agencies inc

over fifteen years of experience in the computer and electronics industry. He manages the computer systems and networks of the Electrical and Computer Engineering Department and is the System Administrator and Technical Advisor for the CVIP Laboratory. Min Electrical Engineering in 1979 from the University of Louisville, and brings twenty years of industry experience in design of computer products for Texas Instruments and VeriFone Inc. Mike's experience includes Vision Aided testing design and algorithms, ASIC design, Notebook Computer Design, and Printer Design technology. He also worked in Design of Secure Banking terminals. His focus in the lab is assisting Grad Students in Design in both the visual and robotic fields.

Students

Aly

37

Alaa E. Abdel-Hakim received his B.S. in Electrical

NASA-Ames .

Hongjian Shi

Engineering with honors from Assiut University, Egypt in 1996, and M.S. in 2000. He joined the ECE for his Ph.D. in Summer 2002. He works in developing novel colored and 3D features and employing them in practical applications, such as imaging and an internship at robotics and medical

holds a Ph.D. in mathematics from Univ. of British Columbia, Canada, and has joined the lab as a Ph.D. student in the fall of 2002. His research interests include application of finite element methods for studying brain deformation.

Hossam Hassan joined the lab as a Ph.D. student in the summer

Rachid Fahmi

of 2002. His research interests include level sets segmentation, image processing and computer vision.

holds a Ph.D. in mathematics from France and has joined the lab as a PhD student in the fall of 2003. His research interests include application of finite element methods for brain deformation.

Dongqing Chen joined the lab as a Ph.D. student in the summer of

Abdelraheem Ahmed

2003. His research interests include medical imaging.

joined the lab as a Ph.D student in the spring of 2004. His research interests include medical imaging.

Sergey Checkmenev has joined the lab as a Ph.D student in the fall

Asem Ali of 2003. His research interests include medical imaging.

has joined the lab as a .D. student in the spring of 2004. PhHis research interests include computer vision and robotics

Ham Rara is working on his Master of Science. He got his BS EE from The University of Philippines

2006 Graduates

38

Ayman El-Baz received his MohB.S. in Electrical Engineer-ing

medical imaging analysiinclude statistical modeli

amed Sabry

with honors from Mansoura University, Egypt in 1997, and M.S. from same university in 2000. He joined the Ph.D. Program in the ECE Department in Summer 2001. Mr. Elbaz is a research assitant at the CVIP Lab working on s of lung cancer. His interests ng, genetic algorithms. Ayman

Graduated in December 2006

joined the lab as a Ph.D. student in the Fall of 2001.

Award" from Silicon 2006

Currently, he is working on 3D volume cerebrovascular segmentation from MRA modality. His research interests include medical imaging, visualization of large-scale medical data sets, pattern recognition, and image processing. He won in 2002, the "Excellence in Visualization Graphics. Sabry Graduated in May

Computer Vision and Image Processing Laboratory (CVIP Lab)

University of Louisville, Louisville, KY 40292 Phone: (502) 852-7510, 2789, 6130

Fax: (502) 852-1580 Email: cvip@cvip.uofl.edu ht u tp://www.cvip.uofl.ed