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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

引用

International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung 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)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Zhujin Zhang Shuo Wang Xingyi Zhang Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

Randić et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n 2 ), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species.

关键词： Rabbits Educational institutions Bovine Humans Sun DNA Mice

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Spectral representation of DNA sequences and its application

Spectral representation of DNA sequences and its application

引用

International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Zhujin Zhang Lin Liu Jiuyong Li Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

Randić et al. proposed a famous spectral graphical representation of DNA sequences, and claimed that it avoids loss of information. In this paper we build two mathematical models for this graphical representation and prove that the claim is correct, and that it also avoids degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the spectral representation. The method adopts M value to characterize a graphical representation and uses 24-component vector as descriptor. The approach is illustrated on the complete coding sequence of beta-globin genes from 7 different species.

关键词： Humans Rabbits Mice Variable speed drives Silicon Sun Bovine

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A novel method of fingerprint minutiae extraction based on Gabor phase

A novel method of fingerprint minutiae extraction based on G...

引用

IEEE International Conference on image processing

作者： Xin Gao Xiaoguang Chen Jia Cao Zirui Deng Chongjin Liu Jufu Feng Key Laboratory of Machine Perception MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.

关键词： Pixel Feature extraction image quality Fingerprint recognition Detectors Frequency estimation software algorithms

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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IEEE International Conference on Mechatronics and Automation

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan China College of Mechanical Engineering TaiYuan University of Technology TaiYuan China Institute for Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology WuHan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： image edge detection image restoration Degradation Atmospheric modeling Optical scattering Optical imaging

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Study on the Method of image Restoration in the Environment of Dust

Study on the Method of Image Restoration in the Environment ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： image edge detection image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

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Evaluating deformation patterns of the thoracic aorta in gated CTA sequences

Evaluating deformation patterns of the thoracic aorta in gat...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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Volume-based Magnetic Resonance Brain image Classification

Volume-based Magnetic Resonance Brain Image Classification

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification image classification Vectors Volume measurement Magnetic resonance

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Techniques for Automatic Magnetic Resonance image Classification

Techniques for Automatic Magnetic Resonance Image Classifica...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

关键词： Classification algorithms Training image segmentation Brain Skull software algorithms Support vector machines

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Improvement of directed diffusion protocol based on data aggregation

Improvement of directed diffusion protocol based on data agg...

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2009 International Conference on Computational Science and Engineering, CSE '09

作者： Hui, Chen Yufeng, Shi Ye, Wang Yang, Laurence T. Naixue, Xiong Key laboratory of ministry of education for image processing and intelligent control Huazhong University of Science and Technology Wuhan China Department of Computer Science St Francis Xavier University Canada Department of Computer Science Georgia State University Georgia GA United States

ISBN: (纸本)9780769538235

How to make full use of the limited energy available and prolonging the survival time of node is a major concern in research on Wireless Sensor Networks (WSNs). This paper presented an improved approach based on Directed Diffusion Protocol (DDP) which could meet the features of WSN, such as data-oriented, many-to-one transmissions and low energy costs, together with data aggregation mechanisms which emphasized on cascading timeouts, to eliminate the redundancy caused by the transmission of large amounts of data. This modified protocol prohibited the direct forwarding of central nodes, balancing data traffic of different sensors between the source and the sink, and proved to be able of reducing total network flows, energy consumptions and prolonging the lifetime of the network through simulations.

关键词： Sensor nodes

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