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IEEE International Conference on image Processing

作者： Melih S. Aslan Hossam Abdelmunim Aly A. Farag Ben Arnold Eslam Mostafa Ping Xiang Computer Vision and Image Processing Lab University of Louisville Louisville KY USA Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Computer and Systems Engineering Department Ain Shams University Cairo Egypt Image Analysis Inc Columbia KY USA

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.

关键词： image segmentation Shape Training Computed tomography Bones Accuracy Vectors

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A novel probabilistic simultaneous segmentation and registration using level set

A novel probabilistic simultaneous segmentation and registra...

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IEEE International Conference on image Processing

作者： Melih S. Aslan Eslam Mostafa Hossam Abdelmunim Ahmed Shalaby Aly A. Farag Ben Arnold Computer Vision and Image Processing Lab. University of Louisville Louisville KY USA Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Computer and Systems Engineering Department Ain Shams University Cairo Egypt Image Analysis Inc. Columbia KY USA

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.

关键词： Shape image segmentation Level set Computed tomography Accuracy Probabilistic logic

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Two-way Clustering using Fuzzy ASI for Knowledge Discovery in Microarrays

Two-way Clustering using Fuzzy ASI for Knowledge Discovery i...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： J. Shaik M. Yeasin Computer Vision Pattern and Image Analysis Lab Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents two-way clustering of microarray data using fuzzy adaptive subspace iteration (ASI) based algorithm for knowledge discovery in microarrays. It is widely believed that each gene is involved in more than one cellular function or biological process. The proposed fuzzy ASI assigns a relevance value to each gene associated with each cluster. These functional categories are ranked based on their potential in providing maximal separation between the two tissues classes; which is an indication of differentially expressed genes (DEGs). Empirical analyses on simulated, 100 artificial microarray datasets are used to quantify the results obtained using the fuzzy-ASI algorithm. Further analyses on different microarray cancer datasets revealed several important genes that are relevant with various cancers.

关键词： Clustering algorithms Biological processes computer vision Algorithm design and analysis Cancer Computational intelligence Bioinformatics Computational biology image analysis Biology computing

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Bit-rate allocation control and quality improvement for color channels in HMVQ image compression

Bit-rate allocation control and quality improvement for colo...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Jiangling Guo P. Shrivastava K. Kepley Shuyu Yang S. Mitra B. Nutter Department of Electrical and Computer Engineering Computer Vision and Image Analysis Laboratory Texas Tech University USA Computer Vision and Image Analysis Lab Department of Electrical and Computer Engineering Texas Tech University USA

Medical images usually require higher fidelity than commonly used natural images, especially with respect to detail preservation. Color medical images also require a higher degree of color preservation. In this paper, we present the extension of the novel gray-scale image compression technique, hybrid multi-scale vector quantization (HMVQ) to color image compression. Limitations of common color image compression methods in controlling bit-rate allocation for color channels are discussed, and the ability of HMVQ in overcoming such limitations while improving the color quality is demonstrated.

关键词： image coding Biomedical imaging Medical diagnostic imaging Entropy image color analysis Gray-scale Pixel image storage Transform coding Quantization

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An automated, segmentation-based, rigid registration system for Cervigram/spl trade/ images utilizing simple clustering and active contour techniques

An automated, segmentation-based, rigid registration system ...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： P. King S. Mitra B. Nutter Computer Vision and Image Analysis Lab Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA

In this article, a technique for the automated registration of Cervigram/spl trade/ images will be introduced. The motivation for the development of such a technique is warranted by the fact that registration is often a first step to other, more sophisticated, algorithms useful in medical applications. Such algorithms are typically processes developed for the tracking and monitoring of patient health. The registration described in this article is segmentation-based and utilizes a combination of clustering- and active contour-based methodologies. The clustering algorithm is used to obtain an initial contour that will subsequently serve as initialization for an active contour. The active contour, in conjunction with various internal and external forces, should converge to a more precise segmentation of the region of interest which, in this application, is the cervix. Once the segmentations are completed, more traditional registration techniques, such as those of Fourier or correlation-based techniques, may be used to register the segmented images with more accuracy as the adverse effects stemming from the highly variable background features are no longer a source of error. For illustration purposes, the results from two patients are demonstrated at the end of this article.

关键词： image segmentation Active contours computer vision Clustering algorithms Cervical cancer image analysis image converters Biomedical imaging image color analysis Medical services

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analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

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1995 IEEE Southwest Symposium on image analysis and Interpretation, IAI 1994

作者： Avadhanam, N. Mitra, S. Center for Image Processing and Integrated Computing Dept. of Electr. and Comput. Eng. California Univ. DavisCA95616 United States Computer Vision and Image Analysis Lab Dept. of Electrical Engineering Texas Tech University LubbockTX79401 United States

ISBN: (纸本)0818662506

A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features. © 1994 IEEE.

关键词： image texture

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analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

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IEEE Southwest Symposium on image analysis and Interpretation

作者： N. Avadhanam S. Mitra Center for Image Processing & Integrated Computing Department of Electrical & Computer Engineering University of California Davis CA USA Computer Vision and Image Analysis Lab Department of Electrical Engineering Texas Tech University Lubbock TX USA

关键词： image analysis image texture analysis Robustness Fractals Maximum likelihood estimation image segmentation Polymers image motion analysis computer vision Clustering algorithms

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ADAPTIVE FUZZY LEADER CLUSTERING OF COMPLEX DATA SETS IN PATTERN-RECOGNITION

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第5期3卷 794-800页

作者： NEWTON, SC PEMMARAJU, S MITRA, S Computer Vision and Image Analysis Lab Department of Electrical Engineering Texas Tech University Lubbock TX USA

Most real data structures encountered in speech and image recognition and in medical and many other decision making tasks are quite complex in nature and rather difficult to organize for designing autonomous and optimal control and recognition systems. This paper presents a modular, unsupervised neural network architecture which can be used for clustering and classification of complex data sets. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.

关键词： Fuzzy sets Clustering algorithms Fuzzy systems Neural networks Control systems Programmable control Adaptive control Resonance Prototypes Equations

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Three-dimensional target recognition from fusion of dense range and intensity images 14

Three-dimensional target recognition from fusion of dense ra...

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Applications of Digital image Processing XIV 1991

作者： Ramirez, Manuel Mitra, Sunanda Computer Vision and Image Analysis Lab Texas Tech University Department of Electrical Engineering LubbockTX79409 United States

This paper describes an approach of feature extraction from range and intensity data for three-dimensional model-based target recognition systems. The range image of the object under study is acquired using a methodology based on a passive stereo-vision model. A coarse to fine strategy is used to explore the features in the images, and cepstral analyisis provides information about disparities between corresponding areas at each step. The surfaces of the 3-D target recovered from the passive stereo-vision model are represented by aspect graphs which are used in vector form as the input for a neural network classifier. © 1991 SPIE. All rights reserved.

关键词： Stereo image processing

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Medical computer vision. Recognition Techniques and Applications in Medical Imaging 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Bjoern H. Menze Georg Langs Le Lu Albert Montillo Zhuowen Tu Antonio Criminisi

ISBN: (数字)9783642366208

ISBN: (纸本)9783642366192

This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical computer vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical image Computing and computer Assisted Intervention, MICCAI 2012.;The 24 papers have been selected out of 42 submissions. At MCV 2012, 12 papers were presented as a poster and 12 as a poster together with a plenary talk. The book also features four selected papers which were presented at the previous CVPR Medical computer vision workshop held in conjunction with the International Conference on computer vision and Pattern Recognition on June 21 2012 in Providence, Rhode Island, USA. The papers explore the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies, as well as 3D reconstruction and biophysical model personalization.

关键词： image Processing and computer vision Health Informatics Pattern Recognition computer Applications

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