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IEEE International Symposium on Biomedical Imaging

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

ISBN: (纸本)9781424441259

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.

关键词： Hippocampus Temporal lobe Epilepsy image segmentation Principal component analysis Shape control Probability density function Level set Skull Algorithm design and analysis

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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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Parsed and fixed block representations of visual information for image retrieval

Parsed and fixed block representations of visual information...

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Human Vision and Electronic Imaging XIV

作者： Bae, Soo Hyun Juang, Biing-Hwang Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

The theory of linguistics teaches us the existence of a hierarchical structure in linguistic expressions, from letter to word root, and on to word and sentences. By applying syntax and semantics beyond words, one can further recognize the grammatical relationship between among words and the meaning of a sequence of words. This layered view of a spoken language is useful for effective analysis and automated processing. Thus, it is interesting to ask if a similar hierarchy of representation of visual information does exist. A class of techniques that have a similar nature to the linguistic parsing is found in the Lempel-Ziv incremental parsing scheme. Based on a new class of multidimensional incremental parsing algorithms extended from the Lempel-Ziv incremental parsing, a new framework for image retrieval, which takes advantage of the source characterization property of the incremental parsing algorithm, was proposed recently. With the incremental parsing technique, a given image is decomposed into a number of patches, called a parsed representation. This representation can be thought of as a morphological interface between elementary pixel and a higher level representation. In this work, we examine the properties of two-dimensional parsed representation in the context of imagery information retrieval and in contrast to vector quantization;i.e. fixed square-block representations and minimum average distortion criteria. We implemented four image retrieval systems for the comparative study;three, called IPSILON image retrieval systems, use parsed representation with different perceptual distortion thresholds and one uses the convectional vector quantization for visual pattern analysis. We observe that different perceptual distortion in visual pattern matching does not have serious effects on the retrieval precision although allowing looser perceptual thresholds in image compression result poor reconstruction fidelity. We compare the effectiveness of the use of the pars

关键词： image retrieval

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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

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.

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

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On capacity of cognitive radio networks with average interference power constraints

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IEEE Transactions on Wireless Communications 2009年第4期8卷 1620-1625页

作者： Wang, Cheng-Xiang Hong, Xuemin Chen, Hsiao-Hwa Thompson, John Joint Research Institute for Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom Department of Electrical and Computer Engineering University of Waterloo ON Canada Department of Engineering Science National Cheng Kung University Taiwan Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh United Kingdom

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected;while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users. © 2006 IEEE.

关键词： Cognitive radio

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A Freshman 'Introduction to ECE Design' Course Based on the LEGO Mindstorms NXT

A Freshman 'Introduction to ECE Design' Course Based on the ...

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IEEE Digital signal processing Workshop

作者： Douglas B. Williams Center of Signal & Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781424436767

Faculty members from Georgia Tech's school of electrical and computer engineering (ECE) have worked jointly with engineers from National Instruments to develop a new freshman course. This course, entitled 'Introduction to ECE Design,' is constructed around the LEGO Mindstorms NXT robotics kit. This course addresses the diverse objectives of providing students with a systems-level design experience at the *** of their academic programs and introducing them to a broad range of ECE disciplines. A primary goal is to enable students to make better-informed decisions when choosing whether or not to major in electrical engineering or computer engineering.

关键词： Robots Design engineering Educational institutions Instruments Programming environments signal processing Hardware Current measurement Charge measurement Power measurement

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A hardware-efficient implementation of the fast affine projection algorithm

A hardware-efficient implementation of the fast affine proje...

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

作者： Haw-Jing Lo David V. Anderson Center of Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781424423538

This paper presents a high-throughput, low-latency, hardware-efficient fixed-point implementation of the fast affine projection (FAP) algorithm. The proposed architecture utilizes reusable distributed arithmetic (RDA) in combination with optimizations in the update process to enable the coefficients to be updated in a fixed number of cycles independent of filter length. Fixed-point simulations show that the effect of replacing some of the multiplications with arithmetic shifts is minor, with RDA-FAP maintaining a faster convergence rate than NLMS. The proposed design is also compared against a multiplier-based design in terms of number of computations and number of clock cycles needed for a single FAP update cycle.

关键词： Projection algorithms Financial advantage program computer architecture signal processing algorithms Adaptive filters Arithmetic Convergence Least squares approximation Gaussian processes Computational modeling

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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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WHISPERS '09 - 1st Workshop on Hyperspectral image and signal processing: Evolution in Remote Sensing

作者： Jiao, Xiaoli Du, Yingzi Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratry Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD 21228 United States Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9781424446872

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. © 2009 IEEE.

关键词： Mixtures

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Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking

Forward-backward sequential Monte Carlo smoothing for joint ...

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2009 12th International Conference on Information Fusion, FUSION 2009

作者： Clark, Daniel E. Ba, Tuong Vo Ba, Ngu Vo Joint Research Institute in Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Hwy Crawley WA 6009 Australia Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC 3052 Australia

ISBN: (纸本)9780982443804

The problem of jointly detecting whether a target is present in a scene and, if there is, estimating its state can be viewed as a multi-object estimation problem where there is a maximum of one target. This joint detection and estimation problem can be solved using a special case of the multi-object Bayes filter. In this paper we investigate the joint target detection and estimation problem with forward-backward smoothing and propose a sequential Monte Carlo implementation. Finite Set Statistics not only facilitates the development of appropriate joint detection and estimation filters, but also the direct extension of these filtering solutions to their related smoothing counterparts. Preliminary results indicate that using the smoothing has two distinct advantages over just using filtering: Firstly, we are able to more accurately identify the appearance and disappearance of a target in the scene and secondly, we can provide improved state estimates when the target exists. ©2009 ISIF.

关键词： Monte Carlo methods

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Spectral derivative feature coding for hyperspectral signature analysis

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Pattern Recognition 2009年第3期42卷 395-408页

作者： Chang, Chein-I Chakravarty, Sumit Chen, Hsian-Min Ouyang, Yen-Chieh Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Environmental Restoration and Disaster Reduction Research Center National Chung Hsing University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Radiology China Medical University Hospital Taichung Taiwan

This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC. © 2008 Elsevier Ltd. All rights reserved.

关键词： Spectrum analysis

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