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Accurate and fast 3D colon segmentation in CT colonography

Accurate and fast 3D colon segmentation in CT colonography

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IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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Kernel-based Linear Spectral Mixture analysis for hyperspectral image classification

Kernel-based Linear Spectral Mixture Analysis for hyperspect...

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Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Keng-Hao Liu Englin Wong Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.

关键词： image analysis Spectral analysis Hyperspectral imaging image classification Least squares methods Hyperspectral sensors Kernel Subspace constraints Remote sensing image resolution

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Soft-tissue modeling and image-guided control of steerable needles

Soft-tissue modeling and image-guided control of steerable n...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nasser Sadati Meysam Torabi Reza Vaziri Reza Dehestani-Ardekani Electrical and Computer Engineering Department University of British Columbia Vancouver BC Canada Robotics and Machine Vision Laboratory Biomedical Engineering Division of the School of Electrical Engineering Sharif University of Technology Tehran Iran

In this paper, we exploit a fuzzy controller on a flexible bevel-tip needle to manipulate the needle's base in order to steer its tip in a preset obstacle-free and target-tracking path. Although the needle tends to follow a curvature path, spinning the needle with an extremely high rotational velocity makes it symmetric with respect to the tissue to follow a straight path. The fuzzy controller determines an appropriate spinning to generate the planned trajectory and, the closed-loop system tries to match the needle body with that trajectory. The swine's brain tissue model, extracted from an in-vitro experimental setup, is a non-homogenous, uncertain and fast-updatable network to model real tissues, needle and their interactions providing the essential visual feedback for the control system. The simulation results illustrate a precise path tracking of the bevel-tip needle based on the fuzzy controller's commands with two degrees of freedom.

关键词： Needles Fuzzy control Minimally invasive surgery Brain modeling Spinning Surges Manipulator dynamics Force feedback Intelligent robots Biomedical engineering

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Xiaoli Jiao Yingzi Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science & Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration.

关键词： image analysis Spectral analysis Hyperspectral imaging Independent component analysis Personal communication networks Inspection Statistics Signal analysis Hyperspectral sensors Principal component analysis

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Comparison of local image descriptors for full 6 degree-of-freedom pose estimation

Comparison of local image descriptors for full 6 degree-of-f...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Fredrik Viksten Per-Erik Forssen Bjorn Johansson Anders Moe Division of Information Coding Department of Electrical Engineering University of Linköping Linkoping Sweden Computer Vision Laboratory Department of Electrical Engineering University of Linköping Linkoping Sweden SICK IVP Linkoping Sweden

Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the Web to allow future comparison with novel algorithms.

关键词： Robustness State estimation Object recognition Robotics and automation image databases Spatial databases Toy industry Augmented reality Consumer products Home appliances

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Fingerprint Feature Compression Using Statistical Coding Techniques

Fingerprint Feature Compression Using Statistical Coding Tec...

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IEEE India Conference (INDICON)

作者： C. Saravanan Sanjeev S. Malalur Micahel T. Manry Computer Centre National Institute of Technology Durgapur West Bengal India Department of Electrical Engineering Image Processing & Neural Networks Laboratory University of Technology Arlington USA

ISBN: (纸本)9781424448586;9781424448593

We have proposed a new fingerprint feature compression scheme, which extracts the fingerprint feature using pseudo-spectral fusion approach and compresses the fingerprint feature using statistical coding technique. The compression scheme starts with fingerprint image acquisition, enhancement, and normalization. Further, the direction and density of the fingerprint image are extracted. Fusing these two direction and density data results finger print feature data. The fingerprint feature data is applied directly using a statistical coding technique to get compressed data. The Huffman coding and arithmetic coding is used for compression and found arithmetic coding performs better. This proposed compression scheme achieves nearly 85% compression rate, which is higher compared to other present techniques achieved 45% to 63% compression.

关键词： Fingerprint recognition image coding image matching Multiresolution analysis Arithmetic Neural networks Data mining Feature extraction Huffman coding Discrete wavelet transforms

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Quantifying the astrocytoma cell response to candidate pharmaceutical from F-ACTIN image analysis

Quantifying the astrocytoma cell response to candidate pharm...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Chi Cui Joseph JaJa Thomas Turbyville John Beutler Prabhakar Gudla Kaustav Nandy Stephen Lockett Department of Electrical and Computer Engineering University of Maryland College Park USA Optical Microscopy and Analysis Laboratory NCI Frederick USA National Cancer Institute Frederick USA

The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation of the effects and dynamics of drug interactions. We have developed an image analysis framework for quantifying F-actin organization patterns in confocal microscope images in response to different candidate pharmaceutical treatments. The main problem solved was to determine which quantitative features to compute from the images that both capture the visually-observed F-actin patterns and correlate with predicted biological outcomes. The resultant numerical features were effective to quantitatively profile the changes in the spatial distribution of F-actin and facilitate the comparison of different pharmaceuticals. The validation for the segmentation was done through visual inspection and correlation to expected biological outcomes. This is the first study quantifying different structural formations of the same protein in intact cells. Preliminary results show uniquely significant increases in cortical F-actin to stress fiber ratio for increasing doses of OSW-1 and Schweinfurthin A(SA) and a less marked increase for cephalostatin 1 derivative (ceph). This increase was not observed for the actin inhibitors: cytochalasin B (cytoB) and Y-27632 (Y). Ongoing studies are further validating the algorithms, elucidating the underlying molecular pathways and will utilize the algorithms for understanding the kinetics of the F-actin changes. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of action of candidate pharmaceuticals.

关键词： Pharmaceuticals image analysis Shape control Drugs Cancer Microscopy Biology computing image segmentation Inspection Proteins

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Interest point detection for hyperspectral imagery

Interest point detection for hyperspectral imagery

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Roysam, Badrinath Mukherjee, Amit Center for Subsurface Sensing and Imaging Systems Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico at Mayaguez Mayaguez PR.00681-9048 United States Center for Subsurface Sensing and Imaging Systems Department of Electrical Computer and Systems Engineering Rensseleaer Polytechnic Institute Troy NY. 12180-3590 United States

ISBN: (纸本)9780819476005

This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature. © 2009 SPIE.

关键词： Object detection

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Kernel principal component analysis for the construction of the extended morphological profile

Kernel principal component analysis for the construction of ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Fauvel J. Chanussot J. A. Benediktsson INRIA Rhônes Alpes & Laboratoire Jean Kuntsmann MISTIS Grenoble France GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Department of Electrical and Computer Engineering University of Iceland Iceland

Kernel Principal Component analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are used to construct the Extended Morphological Profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. The overall classification accuracy increases from 79% to 96% with the proposed approach.

关键词： Kernel Principal component analysis Hyperspectral sensors Hyperspectral imaging EMP radiation effects Feature extraction Eigenvalues and eigenfunctions Data analysis Remote sensing Data engineering

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