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检索条件"机构=Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering"

共 221 条记录，以下是131-140 订阅

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A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRIPPING

A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRI...

引用

IEEE International Symposium on Biomedical Imaging

作者： Aaron Carass M. Bryan Wheeler Jennifer Cuzzocreo Pierre-louis Bazin Susan S. Bassett Jerry L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Johns Hopkins University USA Division of Psychiatric Neuroimaging Psychiatry and Behavioral Sciences Johns Hopkins University USA MedIC Neuroradiology Division Radiology and Radiological Science Johns Hopkins University USA

ISBN: (纸本)9781424406715;1424406714

Extraction of the cerebrum, cerebellum, and brain stem from structural magnetic resonances images (MRIs) is an important initial step in neuroimaging. We present an automated algorithm that solves this difficult problem, often referred to as skull stripping, which is novel for its use of registration, segmentation, and morphological operations. Our algorithm is also concerned with an accurate representation of the grey matter boundary, which is a unique feature. We also present results demonstrating the accuracy of this approach

关键词： Skull image segmentation Magnetic resonance imaging Humans Magnetic heads Surface morphology Neuroimaging Morphological operations Magnetic resonance image analysis

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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424406715;1424406714

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.

关键词： Neoplasms image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL image SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

引用

IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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Automatic estimation of midsagittal plane and AC-PC alignment on nonrigid registration

Automatic estimation of midsagittal plane and AC-PC alignmen...

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IEEE International Symposium on Biomedical Imaging

作者： P. Anbazhagan A. Carass P.-L. Bazin J.-L. Prince Electrical and Computer Engineering Image Analysis and Communications Laboratory MedIC Neuroradiology Division Radiology and Radiological Science Johns Hopkins University USA

Identifying the anterior and posterior commissures as well as the midsagittal plane in magnetic resonance brain images is a critical first step in the normalization process underlying group studies. We present a fully automatic algorithm that locates these structures with high accuracy. Estimates of all three landmarks are achieved first with a nonrigid registration algorithm and further optimization is used to refine these estimates. Extensive validation experiments involving both simulated and real data sets demonstrate the accuracy and robustness of the proposed method

关键词： image analysis Pathology Magnetic resonance Shape Laboratories Biomedical imaging Radiology Brain modeling Robustness Neuroscience

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A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes

A Progressive Framework for Two-Way Clustering Using Adaptiv...

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International Joint Conference on Neural Networks (IJCNN)

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

This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.

关键词： Clustering algorithms Cancer Colon Data analysis Robustness Biological tissues Proteins computer vision image analysis Performance analysis

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Functionally Classifying Genes from Microarray Data Using Linear and Non-linear Data Projection

Functionally Classifying Genes from Microarray Data Using Li...

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ACS/IEEE International Conference on computer Systems and Applications

作者： J. Shaik M. Yeasin Computer Vision Pattern and Image Analysis (CVPIA) laboratory Department of Electrical and Computer Engineering University Of Memphis Memphis TN USA

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Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray Data

Performance Evaluation of Subspace-based Algorithm in Select...

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International Joint Conference on Neural Networks (IJCNN)

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

This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types.

关键词： Clustering algorithms Robustness Algorithm design and analysis Data analysis Partitioning algorithms computer vision image analysis Performance analysis Biomedical imaging Diseases

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High SNR flexible top hat monopole probe for 1.5 T MRI

High SNR flexible top hat monopole probe for 1.5 T MRI

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作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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Fuzzy Edge Preserving Smoothing Filter Using Robust Region Growing

Fuzzy Edge Preserving Smoothing Filter Using Robust Region G...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： A. Mehrtash S. Vahdat H. Soltanian-Zadeh Electrical and Computer Engineering Department University of Tehran Tehran Iran School of Electrical Engineering Sharif University of Technology Tehran Iran Henry Ford Health System Radiology Image Analysis Laboratory Detroit USA USA Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Smoothing, while preserving edges, has always been a major challenge in image processing. In this paper, we propose a new approach that uses segmentation in order to avoid inter-region smoothing thus preserving the edges. It is common to smooth the image prior to region growing. The opposite procedure does not work properly in the presence of noise since region growing is very noise sensitive. To overcome this difficulty we adapted a robust region growing algorithm. Since region growing is very resource consuming, we do not perform it for every pixel. Instead, we divide the image into a number of overlapping blocks for which we carry out the segmentation. Then, we use the results for some of the core pixels in that block. Now that the regions are known, we proceed to smooth each region by a fuzzy approach emphasizing the pixels residing in it. The performance of this filter is compared to that of the bilateral filter and it is shown that the new method generates superior results.

关键词： Smoothing methods Filters Anisotropic magnetoresistance Filtering Pixel image segmentation Noise robustness Noise reduction image processing Partial differential equations

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Robust Segmentation and Volumetric Registration in a Multi-view 3D Freehand Ultrasound Reconstruction System

Robust Segmentation and Volumetric Registration in a Multi-v...

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Asilomar Conference on Signals, Systems & computers

作者： Honggang Yu Marios S. Pattichis M. Beth Goens Department of Electrical and Computer Engineering image and video Processing and Communications Laboratory University of New Mexico Albuquerque NM USA Pediatric Cardiology Clinic of the Children's Hospital Heart Center University of New Mexico Albuquerque NM USA

In this paper, we present a new reconstruction method with volumetric registration and semi-automatic segmentation in a multi-view 3D freehand ultrasound imaging system. The new volumetric registration approach is performed on binarized walls using non-linear least squares. It can provide accurate multi-view reconstructions despite significant rigid target motion between different acoustic window acquisitions. A hybrid adaptive gradient vector flow (GVF) geometric active contour (GAC) model is used for image sequence segmentation. It allows for relatively simple initialization of the deformable model, while avoiding edge leaking at poor edges and small boundary gaps. The algorithms were validated on four ultrasound phantom data sets (eight sequences of a total of 336 images) and two echocardiography data sets (four sequences for a total of 75 images). Quantitative evaluation shows that automatic segmentation is comparable with manual segmentation. Using breath-holding and cardiac gating, volume estimates from multi- view reconstructions of the left-ventricle were found to be in better agreement with clinical estimates than volumes estimated from single view reconstructions.

关键词： Robustness Ultrasonic imaging image segmentation image reconstruction Reconstruction algorithms Acoustic imaging Least squares methods Active contours Solid modeling image sequences

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