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检索条件"机构=Computer Vision Image Processing Laboratory"

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A new shape based segmentation framework using statistical and variational methods

A new shape based segmentation framework using statistical a...

引用

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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ROBUST SPACE-VARIANT SUPER-RESOLUTION

ROBUST SPACE-VARIANT SUPER-RESOLUTION

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2006 IET International Conference on Visual Information Engineering

作者： K.V. Suresh A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

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A NOVEL FRAMEWORK FOR ACCURATE LUNG SEGMENTATION USING GRAPH CUTS

A NOVEL FRAMEWORK FOR ACCURATE LUNG SEGMENTATION USING GRAPH...

引用

IEEE International Symposium on Biomedical Imaging

作者： Asem M. Ali Ayman S. El-baz Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville USA Bioengineering Department University of Louisville USA

ISBN: (纸本)9781424406715;1424406714

The closeness of the gray levels between lung tissues and the chest tissues makes lung segmentation based only on image signals difficult. This work proposes an automatic segmentation framework of lung consisting of three stages. First is the image signal modelling, and initial labelling stage. The labelling algorithm is based on a probabilistic model, which models the image signal with a linear combination of Gaussian distributions with positive and negative components. In the second stage a new analytically estimated potentials for Potts model parameter is used to identify the spatial interaction between the neighboring pixels. Finally the third stage, where an energy function using the previous models is formulated, and is globally minimized using s/t graph cuts. Experiments show that the developed technique segments CT lung images more accurately than other known algorithms.

关键词： Lungs image segmentation Computed tomography Labeling Pathology Object segmentation Gaussian distribution X-ray imaging Cancer detection computer vision

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Modified fuzzy c-mean in medical image segmentation

Modified fuzzy c-mean in medical image segmentation

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

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

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov random field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.

关键词： Biomedical imaging image segmentation Filters Magnetic resonance imaging Fuzzy set theory Brain Classification algorithms Clustering algorithms Partitioning algorithms Markov random fields

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Modified fuzzy c-mean in medical image segmentation

Modified fuzzy c-mean in medical image segmentation

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

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

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov Random Field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.

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ONE-SIDED TRANSPARENCY: A NOVEL VISUALIZATION FOR TUBULAR OBJECTS

ONE-SIDED TRANSPARENCY: A NOVEL VISUALIZATION FOR TUBULAR OB...

引用

IEEE International Conference on image processing

作者： Robert Curtin Marwa Ismail Aly Farag Chuck Sites Salwa Elshazly Robert Falk Computer Vision and Image Processing Laboratory University of Louisville Kentucky Imaging Technologies Medical Imaging Jewish Hospital

ISBN: (纸本)9781479957521

This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This "One-sided-transparency" technique renders a more complete view of the tube's interior. When applied to virtual colonoscopy (VC), it compares favorably to existing methods. It provides more complete images, reduces computational time, and reduces memory requirements while preserving VCs benefits for patients and practitioners. The approach also has various potential uses outside of VC.

关键词： Virtual colonoscopy Colorectal cancer Screening Fly-over One-sided transparency Centerlines

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Motion constraint patterns

Motion constraint patterns

引用

1993 IEEE Workshop on Qualitative vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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Volume registration by surface point signature and mutual information maximization with applications in intra-operative MRI surgeries

Volume registration by surface point signature and mutual in...

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IEEE International Conference on image processing

作者： A.M. Eldeib S.M. Yamany A.A. Farag Computer Vision & Image Processing Laboratory University of Louisville Louisville KY USA Computer Science Old Dominion University VA USA

ISBN: (纸本)0780362977

Intra-operative MRI (iMRI) is a new technology that allows near real time updates of scans during a surgical procedure under the magnet. From an initial comprehensive scan, 3D volumes are obtained. During the surgery, only the portion of the organ under surgery needs to be re-scan (region of interest-ROI) in order to guide the progress of the surgery, and validate the procedure. We describe a registration approach which can be used for iMRI applications. Registration is performed through a hybrid of the surface point signature approach (SPS) and the maximization of the mutual information (MI) criterion. We demonstrate the approach on some real data.

关键词： Mutual information Surges Application software Magnetic resonance imaging Surgery Shape computer vision image processing Laboratories computer science

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Automatic Texture Synthesis for Face Recognition from Single Views

Automatic Texture Synthesis for Face Recognition from Single...

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International Conference on Pattern Recognition

作者： Xiaozheng Zhang Yongsheng Gao M.K.H. Leung Computer Vision & Image Processing Laboratory School of Engineering Griffith University Australia School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)0769525210

One possible solution for pose- and illumination-invariant face recognition is to employ appearance-based approaches, which rely greatly on correct facial textures. However, existing facial texture analysis algorithms are suboptimal, because they usually neglect specular reflections and require numerous training images for virtual view synthesis. This paper presents a novel texture synthesis approach from a single frontal view for face recognition. Using a generic 3D face shape, facial textures are analyzed with consideration of all of the ambient, diffuse, and specular reflections. Virtual views are synthesized under different poses and illuminations. The proposed approach was evaluated using the CMU-PIE face database. Encouraging results show that the proposed approach improves face recognition performances across pose and illumination variations

关键词： Face recognition Optical reflection Lighting computer vision image texture analysis Shape Surface texture Face detection Pattern recognition image databases

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Construction of multi-layer feedforward binary neural network by a genetic algorithm

Construction of multi-layer feedforward binary neural networ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi Kin Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

An approach is introduced to determine the topology of a feedforward binary neural network automatically. The approach is based on a construction algorithm that constructs one layer of hidden nodes at a time until the problem is solved. In each layer, the algorithm determines the necessary number of nodes through a growth process by finding the best hidden node that would help to partition the input training data set. This is done using a genetic algorithm. The proposed algorithm can determine the necessary number of hidden layers and number of hidden nodes at each layer automatically. Tests on a number of benchmark problems illustrated the effectiveness of the proposed technique, both in terms of network complexity and recognition accuracy, compared with a geometrical learning approach.

关键词： Neural networks Multi-layer neural network Feedforward neural networks Genetic algorithms Network topology Partitioning algorithms Training data Error correction computer vision image processing

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