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Elastic Registration of the Corpus Callosum

Elastic Registration of the Corpus Callosum

引用

IEEE International Conference on image processing

作者： Hossam E. Abd El Munim Aly A. Farag Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY 40292. Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY USA

Geometric analysis of postmortem normal and autistic human subjects reveal distinctions in deformations in the corpus callosum (CC) that may be used for image analysis-based studies of autism. Preliminary studies showed that the CC of autistic patients is quite distinct from normal controls. We use an implicit vector representation of CC to carry out the registration process which measures the differences between different CC's. This paper introduces a new method for the 2D shape registration problem by matching vector distance functions. A variational frame work is proposed for the global and local registration of CC's. A gradient descent optimization is used which can efficiently handle both the rigid and the non-rigid operations together. The registration of real CC extracted from postmortem data sets demonstrates the potential of the proposed approach.

关键词： Shape measurement Autism computer vision Magnetic resonance imaging image analysis image processing Laboratories Humans Area measurement computer graphics

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Integrating stereo and shape from shading

Integrating stereo and shape from shading

引用

IEEE International Conference on image processing

作者： M.G.-H. Mostafa S.M. Yamany A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

This paper presents a new method for integrating different low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of objects from intensity images. The integration process is based on correcting the 3D visible surface obtained from shape from shading using the sparse depth measurements from the stereo module by fitting a surface into the difference between the two data sets. A feedforward neural network is used to fit a surface to the error difference. An extended Kalman filter algorithm is used for the network learning. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements.

关键词： Surface fitting Shape measurement image reconstruction Layout Stereo image processing Stereo vision Surface reconstruction Neural networks Feedforward neural networks Machine vision

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MRA data segmentation using level sets

MRA data segmentation using level sets

引用

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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Locked, unlocked and semi-locked network weights for four different camera calibration problems

Locked, unlocked and semi-locked network weights for four di...

引用

International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)0780370449

Backpropagation training algorithms typically view network weights as a single vector of isotropic parameters to be minimized. In contrast, we present a neural network in which each network weight has its own physical meaning and its different role during network training. The network is used to solve four different types of calibration problems found in computer vision applications. A network weight may be unlocked, locked or semi-locked during training according to the available information about the problem. Experiments show the network trained with the available backpropagation-based algorithms can provide superior results to some other widely-used calibration techniques.

关键词： Cameras Calibration Neural networks computer vision Backpropagation algorithms Application software Multi-layer neural network Feedforward neural networks Optical computing image processing

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An automated vertebra identification and segmentation in CT images

An automated vertebra identification and segmentation in CT ...

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IEEE International Conference on image processing

作者： Melih S. Aslan Asem Ali Ham Rara Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we propose a new 3D framework to identify and segment VBs and TBs in clinical computed tomography (CT) images without any user intervention. The Matched filter is employed to detect the VB region automatically on axial axis. To identify the VB on coronal and sagittal axis, we use a new developed approach based on 4 points automatically placed on cortical shell. To segment the identified VB, the graph cuts method which integrates a linear combination of Gaussians (LCG) and Markov Gibbs Random Field (MGRF) are used. Then, the cortical and trabecular bones are segmented using local volume growing methods. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.

关键词： Bones image segmentation Computed tomography Three dimensional displays Accuracy Spine Biomedical imaging

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MODELING image IRRADIANCE UNDER NATURAL ILLUMINATION AND ISOTROPIC SURFACE REFLECTANCE

MODELING IMAGE IRRADIANCE UNDER NATURAL ILLUMINATION AND ISO...

引用

IEEE International Conference on image processing

作者： Shireen Y. Elhabian Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

Discounting subsurface scattering and surface emittance, this work seeks to address the question;how to accurately represent image irradiance under unknown general illumination and reflectance, given that a surface point sees its surrounding world through the local upper hemisphere oriented by the surface normal at this point. In this paper, we formulate the image formation process of isotropic surfaces under arbitrary distant illumination in the frequency space while addressing the physical compliance of hemispherical basis for representing surface reflectance, e.g. Helmholtz reciprocity and isotropy. This representation is further reduced in dimensionality through analytically deriving the principal components of the image irradiance. A database of natural illumination and real world surface materials are used to compute a general-purpose basis which captures the full behavior of illumination and reflectance in a lower-dimensional subspace for image irradiance representation. Compared to similar basis in literature, our proposed basis achieves higher accuracy levels at lower illumination orders.

关键词： Lighting Surface treatment Materials Harmonic analysis Eigenvalues and eigenfunctions Rendering (computer graphics) Databases

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A fast level set algorithm for shape-based segmentation with multiple selective priors

A fast level set algorithm for shape-based segmentation with...

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IEEE International Conference on image processing

作者： Rachid Fahmi Aly A. Farag Computer Vision & Image Processing laboratory University of Louisville Louisville KY USA

This paper addresses the shape-based segmentation problem using level sets. In particular, we propose a fast algorithm to solve the piece-wise constant Chan-Vese segmentation model with shape priors and labeling functions. Instead of directly solving the underlying PDE, we calculate the energy and check how it changes when we move image points from inside the region enclosed by the evolving interface to the outside region and vice-vera. This algorithm is then extended to the case of multi-phase Chan-Vese model, with multiple selective shape priors and a corresponding labeling function for each prior. This makes our algorithm different from that in [1] and other similar works in different aspects. On one hand, our algorithm is not restricted to two regions, but allows segmentation into several regions. On the other hand, more than one shape prior can be taken into account in our implementation. In addition, the proposed algorithm improves dramatically the computational speed. Experimental results, on both synthetic and real images, demonstrate the performance of our algorithm and the computational improvements it offers.

关键词： Level set image segmentation Shape Labeling computer vision image processing Laboratories Biomedical imaging Application software Noise shaping

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Supervised fuzzy and Bayesian classification of high dimensional data: a comparative study

Supervised fuzzy and Bayesian classification of high dimensi...

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IEEE International Conference on image processing

作者： M.G.-H. Mostafa T.C. Perkins A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)0780362977

This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce the computational time. Comparison with the conventional FCM clustering technique and the Bayesian classification technique is also presented. Performance results of the three algorithms are presented on simulated and real remote sensing multispectral data, which show improvement in the classification accuracy using the SFCM technique.

关键词： Bayesian methods Clustering algorithms Iterative algorithms Remote sensing computer vision Fuzzy logic Pixel Uncertainty Classification algorithms image processing

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Dynamic Weighting of Facial Features for Automatic Pose-Invariant Face Recognition

Dynamic Weighting of Facial Features for Automatic Pose-Inva...

引用

Canadian Conference on computer and Robot vision, CRV

作者： Eslam A. Mostafa Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

This paper proposes an automatic pose-invariant face recognition system. In our approach, we consider the texture information around the facial features to compute the similarity measure between the probe and gallery images. The weight of each facial feature is dynamically estimated based on its robustness to the pose of the captured image. An approach to extract the 9 facial features used to initialize the Active shape model is proposed. The approach is not dependent on the texture around the facial feature only but incorporates the information obtained about the facial feature relations. Our face recognition system is tested on common datasets in pose evaluation CMU-PIE and FERET. The results show out-performance of the state of the art automatic face recognition systems.

关键词： Facial features Face Shape Face recognition Feature extraction Detectors Active appearance model

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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...

引用

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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