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Segmentation of vertebrae using level sets with expectation maximization algorithm

Segmentation of vertebrae using level sets with expectation ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Melih S. Aslan Aly A. Farag Ben Arnold Ping Xiang Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Image Analysis Inc. Columbia KY USA

ISBN: (纸本)9781424441273

In this paper, we propose a robust level sets method to segment vertebral bodies (VBs) in clinical computed tomography (CT) images. Since the VB and surrounding organs have very close gray level information and there are no strong edges in some CT images, the initialization of level sets method becomes very crucial step. If the object and background regions are not initialized correctly, the results would not be acceptable. Also, the size and place of the initial seed may give non-reproducible results. To solve these problems, we use a statistical level sets method which uses the Expectation- Maximization (EM) algorithm for the initialization and parameter estimation. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP) as a known reference. The proposed method is compared with other known alternatives.

关键词： image segmentation Level set Bones Computed tomography Biomedical imaging Robustness Three dimensional displays

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A new shape based segmentation framework using statistical and variational methods

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

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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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Model-based multiview stereo via level sets with statistical shape prior

Model-based multiview stereo via level sets with statistical...

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IEEE International Conference on image processing

作者： Moumen El-Melegy Nagi Al-Ashwal Aly A. Farag Electrical Engineering Department Assiut University Asyut Egypt Computer Vision and Image Processing Laboratory University of Louisville KY USA

In this paper, we propose a methodology to incorporate 3D shape prior information in multi-view stereo. This is important for applications that deal with specific category of objects. The methodology is based on a new formulation of a level-set based energy functional. Shape prior model is then embedded in the energy functional to allow the reconstruction of an object with shape variations consistent with the training model examples. Several experiments to evaluate the proposed methodology are presented.

关键词： Shape image reconstruction Level set Stereo vision Solid modeling Training Ellipsoids

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Semantic segmentation using regions in natural scenes

Semantic segmentation using regions in natural scenes

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2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011

作者： Wu, Shilin Zhu, Feng Hao, Yingming Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China

ISBN: (纸本)9781612840857

By introducing an over-segmentation algorithm into the conditional model (CM), we propose a new region-based CM model (R-CM), and investigate its performance on semantic segmentation of images. In order to incorporate structure information of objects, we segment an image into regions by using an over-segmentation algorithm. Based on the results of CM model, we first consider assigning all pixels in one region with the same label, and then other feature potentials are included to counteract the influence of false over-segmentation. We compare our results to related work on the Olive & Torralba database and show that aside from improved accuracy of the whole database, our model obtains a perceptual improvement, with boundary of different objects correctly labeled. © 2011 IEEE.

关键词： Semantic Segmentation

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Multiple class segmentation using a scene-based framework

Multiple class segmentation using a scene-based framework

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2011 International Conference on Electronics and Optoelectronics, ICEOE 2011

作者： Wu, Shilin Zhu, Feng Miao, Xikui Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China

ISBN: (纸本)9781612842738

In this paper, we propose a new scene-based conditional model and investigate its performance on multiple class segmentation of images. By including the scene of an image, we also propose a new texture-environment potential to represent texture environment of a pixel. We compare our results to related work on the Olive & Torralba database and show that our model obtains greatly improved accuracy of the whole database. More significantly, a large perceptual improvement is gained, i.e. details of different objects are correctly labeled. © 2011 IEEE.

关键词： image segmentation

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Frequency domain feature-based face recognition technique for different poses and low-resolution conditions

Frequency domain feature-based face recognition technique fo...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Seyed Omid Shahdi S.A.R. Abu-Bakar Computer Vision Video and Image Processing (CvviP) Research Laboratory Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Pose variations are known to give real challenges in face recognition system. In this paper we proposed a method to recognize non-frontal faces with high performance by relying only on single full frontal gallery faces. By utilizing only small regions of the face or patches, we compute the Fourier coefficients of these patches for each image and transform them into a single vector. Hence, instead of comparing and matching pixels values we use these vectors to form a linear relationship which is then used to estimate the frontal face vector and then compare it with the actual frontal feature vector. The results show an average performance accuracy of 90% across all pose.

关键词： Face Face recognition Databases Feature extraction Training Vectors

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A new segmentation and registration approach for vertebral body analysis

A new segmentation and registration approach for vertebral b...

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IEEE International Symposium on Biomedical Imaging

作者： Melih S. Aslan Asem Ali Aly A. Farag Hossam Abdelmumin Ben Arnold Ping Xiang 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

To diagnose the osteoporosis accurately, the bone mineral density (BMD) measurements of the vertebral bodies (VBs) are required. In this paper, we propose a new segmentation and registration method in order to assist the BMD measurements and fracture analysis (FA) accurately. In this experiment, image appearance and shape information of VBs are used. Our shape model is required to be registered to the testing image to avoid user interaction(s). Our proposed framework has four phases: i) the detection of vertebral body (VB) using the Matched filter, ii) initial segmentation using the intensity and spatial interaction models, iii) the registration of the shape prior and initially segmented image by matching a vector distance function (VDF), and iv) final segmentation using graph cuts which integrates intensity, spatial interaction and shape prior. Preliminary results show that our new algorithm is very promising and can solve many segmentation and registration problems.

关键词： Shape image segmentation Three dimensional displays Computed tomography Bones Labeling image color analysis

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Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Elastic shape registration using an incremental free form de...

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IEEE International Conference on image processing

作者： Hossam Abdelmunim Aly A. Farag Computer & Systems Engineering Department Ain Shams University Cairo Egypt Computer Vision and Image Processing Laboratory Electrical & Computer Engineering Department University of Louisville Louisville KY USA

Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point- based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as criteria to establish point correspondences in each level of the IFFD framework. The IFFD control lattice resolution is in- creased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.

关键词： Shape Lattices Iterative closest point algorithm Equations Spline Mathematical model Three dimensional displays

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Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest

Evaluation of geometric feature descriptors for detection an...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Asem Ali James Graham Aly Farag Salwa Elshazly Robert Falk Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical Imaging Division Jewish Hospital Louisville KY USA

This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.

关键词： Feature extraction Lungs Training Databases Solid modeling Principal component analysis Computed tomography

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