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IEEE International Conference on image processing

作者： H. Hassan A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we use a level set based segmentation algorithm to extract the vascular tree from magnetic resonance angiography, "MRA". Classification model finds an optimal partition of homogeneous classes with regular interfaces. Regions and their interfaces are represented by level set functions. The algorithm initializes level sets in each image slice using automatic seed initialization and then iteratively, each level set approaches the steady state and contains the vessel or nonvessel area. The algorithm is applied on each slice of the volume to build up the tree. The results are validated using a phantom that simulates the "MRA". The approach is fast and accurate. Results on various cases demonstrate the accuracy of the approach.

关键词： Level set image segmentation Partitioning algorithms Iterative algorithms Data mining Imaging phantoms Diseases Tree graphs computer vision image processing

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Stochastic models in image analysis: parameter estimations and case studies in image segmentation

Stochastic models in image analysis: parameter estimations a...

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： A. El-Baz A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

Stochastic models of images are commonly represented in terms of three random processes (random fields) defined on the region of support of the image. The observed image process G is considered as a composite of two random process: a high level process X, which represents the regions (or classes) that form the observed image; and a low level process Y, which describes the statistical characteristics of each region (or class). The representation G = (X, Y) has been widely used in the image processing literature in the past two decades. In this paper we show how to use expectation maximization (EM) algorithm to get accurate model for the low level image by using mixture of normal distribution. The main idea of the proposed algorithm is as follow: first, we will use the EM algorithm to get the most dominance mixtures in the given density (empirical density), and then we assume that the absolute error between the empirical density and the estimated density is another density and we use the EM algorithm to estimate the number of mixtures in this error and the parameters for each mixtures. Then the estimated density for the absolute error is added or subtracted from the estimated density according to the sign (error). Convergence to the true distribution is tested using the Levy distance. A popular model for the high level process X has been the Gibbs-Markov random field (GMRF) model. In this paper we will use the same approach, which described in A. El-Baz et al. (July 2003) to estimate the parameters of GMRF. The approach has been applied on real images (spiral CT slices) and provides satisfactory results.

关键词： Stochastic processes image analysis Parameter estimation image segmentation Random processes image processing Gaussian distribution Convergence Testing Spirals

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A new unsupervised approach for the classification of multispectral data

A new unsupervised approach for the classification of multis...

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International Conference on Information Fusion

作者： R.M. Mohamed A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

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Pararmeter estimation in gibbs-markov image models

Pararmeter estimation in gibbs-markov image models

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International Conference on Information Fusion

作者： A. El-Baz A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

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Two Sequential Stages Classifier for Multispectral Data

Two Sequential Stages Classifier for Multispectral Data

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Refaat M Mohamed Aly A Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.

关键词： Support vector machines Estimation Classification algorithms Pixel Density functionional theory Kernel computer vision

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image segmentation using GMRF models: parameters estimation and applications

Image segmentation using GMRF models: parameters estimation ...

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IEEE International Conference on image processing

作者： A. El-Baz A.A. Farag Computer Vision and Image Processing Laboratory University of louisville Louisville KY USA

Stochastic models of images are commonly represented in terms of three random processes (random fields) defined on the region of support of the image. The observed image process G is considered as a composite of two random process: a high level process G/sup h/, which represents the regions (or classes) that form the observed image; and a low level process G/sup l/, which describes the statistical characteristics of each region (or class). The representation G = (G/sup h/, G/sup l/) has been widely used in the image processing literature in the past two decades. In this paper, we consider the low level process G/sup l/ as mixture of normal distributions, and we use the expectation-maximization (EM) algorithm to estimate the mean, the variance, and proportion for each distribution. A popular model for the high level process G/sup h/ has been the Gibbs-Markov random field (GMRF) model. We introduce a novel unsupervised approach to estimate the parameters of a GMRF model. In this approach, we estimate the model parameters that maximize the posteriori probability of each pixel in a given image. The MAP estimate is obtained using a combination of genetic search and deterministic optimization using the iterated conditional mode (ICM) approach of Besag. The desired estimate of the GMRF parameters is the one corresponding to the MAP estimate. The approach has been applied on real images (Spiral CT slices) and provides satisfactory results.

关键词： image segmentation Parameter estimation Random processes Stochastic processes image processing Gaussian distribution Pixel Genetics Spirals Computed tomography

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Statistically Robust Approach to Lens Distortion Calibration with Model Selection

Statistically Robust Approach to Lens Distortion Calibration...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Moumen Taha El-Melegy Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Electrical Engineering Dept. Assiut University Assiut Egypt Computer Vision and Image Processing Lab. University of Louisville Louisville KY

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement in one form or another,we present an approach to distortion calibration based on the robust the-least-median-of-squares (LMedS) estimator. Our approach is thus able to proceed in a ful ly-automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3D linear segments. Our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a non-linear optimization algorithm to straighten imaged lines. Moreover we propose a method for distortion model selection based on geometrical *** experiments to evaluate the performance of this approach on synthetic and real data are reported.

关键词： Nonlinear distortion Calibration Robustness Lenses Cameras image edge detection Distortion measurement

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Video reconstructions in dentistry

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Orthodontics and Craniofacial Research 2003年第SUPPL1期6卷 108-116页

作者： Farag, A.A. Eid, A. Electrical and computer Engineering Department University of Louisville Computer Vision and Image Processing Laboratory Louisville KY 40292 United States

Objectives: To present two practical techniques for threedimensional (3D) modeling of the human jaw from a sequence of intra-oral images. Design: A data acquisition system consists of: 3D digitizing arm, CCD camera and a laser projector in addition to a software module of two 3D modeling techniques;shape from shading (SFS) and space carving (SC). Setting and sample population - Several experiments have been conducted on a sample of students at the computer vision and image processing (CVIP) laboratory at the University of Louisville, Louisville, KY. Other experiments were performed on solid models of human jaw. Experimental variable: The SFS technique, using perspective projection and camera calibration, extracts the 3D information from a sequence of two-dimensional images of the jaw. Data fusion of range data and 3D registration techniques develop the complete jaw model. The SC approach is implemented on a sequence of calibrated images. On the two reconstructions, we fit a mesh model to the data, in order to create a solid 3D model. Outcome measure: The accuracy of the reconstructed 3D model of human jaw is calculated based on the measurements on real jaws. Results: The SFS-based technique seems to provide more faithful information about the shape of the tooth tops. However, the SC algorithm successfully reconstructed 3D models of the human jaw with sub-millimeter accuracy, which is as accurate as (or even better than) the first technique without using any range measurements or laser projectors. The average error in distance calculation was found to be 0.74 mm, which is an acceptable resolution for many orthodontics and maxillofacial applications. Conclusion: Accurate 3D reconstruction of the human jaw enables many orthodontics and dental imaging researchfindings to be applied directly to a digital jaw model - not to a cast - using computer vision and medical imaging tools. © 2003 Blackwell Munksgaard.

关键词： Data fusion Jaw modeling Registration Shape from shading Space carving

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A unified approach for detection, visualization, and identification of lung abnormalities in chest spiral CT scans

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International Congress Series 2003年第C期1256卷 998-1004页

作者： El-Baz, Ayman Farag, Aly A Falk, Robert La Rocca, Renato Computer Vision Image Processing Laboratory University of Louisville Louisville KY 40292 2301 South 3rd st. Lutz Hall RM 415 United States Medical Imaging Division Jewish Hospital United States Kentuckiana Cancer Institute Louisville KY 40202 Suite 502 100 East Liberty Street United States

This research aims at developing a fully automatic computer-Assisted Diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. This paper presents two novel approaches for segmentation of the lung tissues from the surrounding structures in the chest cavity, and detection of the abnormalities in the lung tissues. The segmentation algorithm is hierarchical. The first step is to isolate the background from the chest cavity. The second step is to isolate the lungs from the surrounding structures (e.g., ribs, liver, and other organs that may appear in chest CT scans) by using Gibbs–Markov Random Field (GMRF). The third step is to isolate the abnormality (lung nodules), arteries, vines, bronchi, and bronchioles from the normal tissues. Finally, the abnormalities in the lungs are detected by using adaptive template matching;its parameters (mean, variance…) are estimated from the given data. In order to increase the speed of detecting lung nodules, we use genetic algorithms (GAs) to determine the target position in the observed image and to select an adequate template image from several reference patterns. © 2003, Elsevier Science B.V. All rights reserved.

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Automatic identification of lung abnormalities in chest spiral CT scans

Automatic identification of lung abnormalities in chest spir...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： A. El-Bazl A.A. Farag R. Falk R. La Rocca Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Medical Imaging Division Jewish Hospital USA Medical Imaging Division Jewish Hospital Kentuckiana Cancer Institute

Our aim is to develop a fully automatic computer-assisted diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. A screening program on 1000 subjects aims at quantification of the effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluation of its possible impact on improving the mortality rate of cancer patients. The paper presents an image analysis system for 3D reconstruction of the lungs and trachea, detection of lung abnormalities, identification/classification of these abnormalities with respect to specific diagnosis, and distributed visualization of the results over computer networks. We present two novel approaches for segmentation of the lung tissues from the surrounding structures in the chest cavity, and detection of abnormalities in the lungs. The segmentation algorithm is hierarchical, first isolating the background from the chest cavity, then isolating the lungs from surrounding structures (e.g., ribs, liver, and other organs). Abnormalities in the lungs are detected by analyzing the segmented lung tissues and extracting the isolated lumps that appear in various connected regions. 3D reconstructions are also generated for these abnormalities, to be used for subsequent identification/classification steps. Results on 50 subjects are shown, and have been evaluated against radiologists. Our image analysis approach has provided comparable results with respect to the experts. The approach is quite fast, and lends itself to distributed visualization over computer networks.

关键词： Lungs Spirals Computed tomography Cancer image analysis image reconstruction Visualization computer networks image segmentation computer aided diagnosis

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