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International Conference on signal processing, image processing and Pattern Recognition (ICSIPR)

作者： Sridhar, D. Krishna, I. V. Murali Balaji Inst Technol & Sci Warangal Andhra Pradesh India Jackson State Univ Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467348621;9781467348614

In this paper, a new method for Brain Tumor Classification using Probabilistic neural Network with Discrete Cosine Transformation is proposed. The conventional method for computerized tomography and magnetic resonance brain images classification and tumor detection is by human inspection. Operator assisted classification methods are impractical for large amounts of data and are also non reproducible. Computerized Tomography and Magnetic Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies in classification. The use of neural Network techniques shows great potential in the field of medical diagnosis. Hence, in this paper the Probabilistic neural Network with Discrete Cosine Transform was applied for Brain Tumor Classification. Decision making was performed in two steps, i) Dimensionality reduction and Feature extraction using the Discrete Cosine Transform and ii) classification using Probabilistic neural Network (PNN). Evaluation was performed on image data base of 20 Brain Tumor images. The proposed method gives fast and better recognition rate when compared to previous classifiers. The main advantage of this method is its high speed processing capability and low computational requirements.

关键词： Brain tumor image classification Probabilistic neural Networks Discrete Cosine Transform Dimensionality Reduction Feature Extraction

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Brain Tumor Classification using Discrete Cosine Transform and Probabilistic neural Network

Brain Tumor Classification using Discrete Cosine Transform a...

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International Conference on signal processing, image processing and Pattern Recognition (SIP)

作者： D. Sridhar IV. Murali Krishna Balaji Institute of Technology and Sciences Warangal Andhra pradesh India Programs in Asia Jackson State University USA

关键词： Brain Inspection Magnetic resonance imaging Probabilistic logic Medical treatment Computed tomography Discrete cosine transforms

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A REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION methods AND APPLICATIONS

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2012年第1期5卷 107-148页

作者： Neshat, Mehdi Adeli, Ali Sepidnam, Ghodrat Sargolzaei, Mehdi Toosi, Adel Najaran Islamic Azad Univ Shirvan Branch Dept Comp Sci Shirvan Iran Islamic Azad Univ Shirvan Branch Dept Comp Engn Shirvan Iran Islamic Azad Univ Shirvan Branch Dept Comp Sci & Hardware Engn Shirvan Iran Islamic Azad Univ Shirvan Branch Dept Comp Sci & Software Engn Shirvan Iran

The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are social behaviors of animals. Optimization principle is seen in these animals. The Artificial Fish Swarm Optimization (AFSA) method is one of the Swarm Intelligence approaches that works based on the population and stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal processing and so on. Also, various methods combining the AFSA with other optimization methods like PSO, Fuzzy Logic, Cellular Learning Automata or intelligent search methods like Tabu search, Simulated Annealing, Chaos Search and etc.

关键词： Artificial Fish Swarm Optimization Swarm Optimization Natural Computing

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变时滞Cohen-Grossberg随机反应扩散神经网络的吸引集和不变集

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四川师范大学学报（自然科学版） 2012年第4期35卷 515-520页

作者：赵雁乐山职业技术学院机电工程系四川乐山614000

从***和***(IEEE Trans Sys Man Cyber,1983,13:815-826.)提出Cohen-Gross-berg神经网络以来,由于它在信号和图像处理、联想记忆、组合优化等中的广泛应用,因此受到了广泛的关注和研究.由于随机干扰和反应扩散,时滞Cohen-Grossberg随机... 详细信息

从***和***(IEEE Trans Sys Man Cyber,1983,13:815-826.)提出Cohen-Gross-berg神经网络以来,由于它在信号和图像处理、联想记忆、组合优化等中的广泛应用,因此受到了广泛的关注和研究.由于随机干扰和反应扩散,时滞Cohen-Grossberg随机反应扩散神经网络的平衡点往往不存在,这时,往往研究其吸引集和不变集的存在性.建立了一种研究时滞Cohen-Grossberg随机反应扩散神经网络的吸引集与不变集的方法.通过应用Ito公式、时滞微分不等式技巧和M-矩阵性质,获得了判定变时滞Cohen-Grossberg随机反应扩散神经网络存在吸引集和不变集的充分条件.所获得的充分条件在实践当中可以用简单的代数方法验证,因此有广泛的应用价值.最后举例用以说明我们的结果的有效性.

关键词：随机反应扩散吸引集不变集

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Decoding the direction of auditory motion in blind humans

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NEUROimage 2011年第2期56卷 681-687页

作者： Wolbers, Thomas Zahorik, Pavel Giudice, Nicholas A. Univ Edinburgh Ctr Cognit & Neural Syst Edinburgh EH8 9JZ Midlothian Scotland Univ Edinburgh Ctr Cognit Ageing & Cognit Epidemiol Edinburgh EH8 9JZ Midlothian Scotland Univ Louisville Heuser Hearing Inst Dept Psychol & Brain Sci Louisville KY 40292 USA Univ Maine Dept Spatial Informat Sci & Engn Orono ME 04469 USA

Accurate processing of nonvisual stimuli is fundamental to humans with visual impairments. In this population, moving sounds activate an occipito-temporal region thought to encompass the equivalent of monkey area MT+, but it remains unclear whether the signal carries information beyond the mere presence of motion. To address this important question, we tested whether the processing in this region retains functional properties that are critical for accurate motion processing and that are well established in the visual modality. Specifically, we focussed on the property of 'directional selectivity', because MT+ neurons in non-human primates fire preferentially to specific directions of visual motion. Recent neuroimaging studies have revealed similar properties in sighted humans by successfully decoding different directions of visual motion from fMRI activation patterns. Here we used fMRI and multivariate pattern classification to demonstrate that the direction in which a sound is moving can be reliably decoded from dorsal occipito-temporal activation in the blind. We also show that classification performance is at chance (i) in a control region in posterior parietal cortex and (ii) when motion information is removed and subjects only hear a sequence of static sounds presented at the same start and end positions. These findings reveal that information about the direction of auditory motion is present in dorsal occipito-temporal responses of blind humans. As such, this area, which appears consistent with the hMT+ complex in the sighted, provides crucial information for the generation of a veridical percept of moving non-visual stimuli. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Adult Auditory Perception/physiology Blindness Brain Mapping/methods Brain Mapping/methods Cerebral Cortex/physiology Humans image processing, Computer-Assisted/methods Magnetic Resonance Imaging Male Middle Aged Movement/physiology Visually Impaired Persons

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Dynamic causal modelling: A critical review of the biophysical and statistical foundations

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NEUROimage 2011年第2期58卷 312-322页

作者： Daunizeau, J. David, O. Stephan, K. E. UCL Inst Neurol Wellcome Trust Ctr Neuroimaging London WC1N 3BG England Univ Zurich Inst Empir Res Econ Lab Social & Neural Syst Res CH-8006 Zurich Switzerland Grenoble Inst Neurosci INSERM U836 Grenoble France Univ Grenoble 1 Grenoble France Univ Hosp Dept Neuroradiol Grenoble France Univ Hosp MRI Unit Grenoble France

The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Bayes Theorem Biophysics Brain Mapping/methods Brain Mapping/methods Brain Mapping/statistics & numerical data Brain Mapping/statistics & numerical data Causality Data Interpretation, Statistical Electroencephalography/statistics & numerical data Humans image processing, Computer-Assisted Magnetic Resonance Imaging/statistics & numerical data Magnetoencephalography Magnetoencephalography Models, Neurological Models, Statistical Reproducibility of Results

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image Parsing via stochastic Scene Grammar 11

Image Parsing via Stochastic Scene Grammar

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Annual Conference on neural Information processing Systems

作者： Yibiao Zhao Song-Chun Zhu Department of Statistics University of California Los Angeles Los Angeles CA 90095 Department of Statistics and Computer Science University of California Los Angeles Los Angeles CA 90095

ISBN: (纸本)9781618395993

This paper proposes a parsing algorithm for scene understanding which includes four aspects: computing 3D scene layout, detecting 3D objects (e.g. furniture), detecting 2D faces (windows, doors etc.), and segmenting background. In contrast to previous scene labeling work that applied discriminative classifiers to pixels (or super-pixels), we use a generative stochastic Scene Grammar (SSG). This grammar represents the compositional structures of visual entities from scene categories, 3D foreground/background, 2D faces, to ID lines. The grammar includes three types of production rules and two types of contextual relations. Production rules: (i) AND rules represent the decomposition of an entity into sub-parts; (ii) OR rules represent the switching among sub-types of an entity; (iii) SET rules represent an ensemble of visual entities. Contextual relations: (i) Cooperative "+" relations represent positive links between binding entities, such as hinged faces of a object or aligned boxes; (ii) Competitive "-" relations represents negative links between competing entities, such as mutually exclusive boxes. We design an efficient MCMC inference algorithm, namely Hierarchical cluster sampling, to search in the large solution space of scene configurations. The algorithm has two stages: (i) Clustering: It forms all possible higher-level structures (clusters) from lower-level entities by production rules and contextual relations, (ii) Sampling: It jumps between alternative structures (clusters) in each layer of the hierarchy to find the most probable configuration (represented by a parse tree). In our experiment, we demonstrate the superiority of our algorithm over existing methods on public dataset. In addition, our approach achieves richer structures in the parse tree.

关键词： parsing tree syntax Rule Solution space Production Rule parsing algorithms DOOR Parsing

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The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution

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NEUROimage 2011年第2期58卷 296-302页

作者： Roebroeck, Alard Formisano, Elia Goebel, Rainer Maastricht Univ Fac Psychol & Neurosci Dept Cognit Neurosci NL-6200 MD Maastricht Netherlands

Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures. (ii) temporal precedence - and causality concepts based on it - are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Brain/physiology Causality Cerebrovascular Circulation/physiology Hemodynamics/physiology Humans image processing, Computer-Assisted/statistics & numerical data Magnetic Resonance Imaging/methods Models, Neurological Models, Statistical Nerve Net/physiology neural Pathways/physiology stochastic Processes

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A Comparison of Variational and Markov Chain Monte Carlo methods for Inference in Partially Observed stochastic Dynamic Systems

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JOURNAL OF signal processing SYSTEMS FOR signal image AND VIDEO TECHNOLOGY 2010年第1期61卷 51-59页

作者： Shen, Yuan Archambeau, Cedric Cornford, Dan Opper, Manfred Shawe-Taylor, John Barillec, Remi Aston Univ Neural Comp Res Grp Birmingham B4 7ET W Midlands England UCL Dept Comp Sci London England Tech Univ Berlin Artificial Intelligence Grp Berlin Germany

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.

关键词： Data assimilation signal processing Nonlinear smoothing Variational approximation Bayesian computation

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Biomedical signal Analysis: Contemporary methods and Applications 1

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2010年

作者： Fabian J. Theis Anke Meyer-B?se

ISBN: (纸本)9780262013284

Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmitting, analyzing, and displaying images have been developed in recent years, allowing scientists and physicians to obtain quantitative measurements to support scientific hypotheses and medical diagnoses. This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and interpretation. After an introduction to the topic and a survey of several processing and imaging techniques, the book describes a broad range of methods, including continuous and discrete Fourier transforms, independent component analysis (ICA), dependent component analysis, neural networks, and fuzzy logic methods. The book then discusses applications of these theoretical tools to practical problems in everyday biosignal processing, considering such subjects as exploratory data analysis and low-frequency connectivity analysis in fMRI, MRI signal processing including lesion detection in breast MRI, dynamic cerebral contrast-enhanced perfusion MRI, skin lesion classification, and microscopic slice image processing and automatic labeling. Biomedical signal Analysis can be used as a text or professional reference. Part I, on methods, forms a self-contained text, with exercises and other learning aids, for upper-level undergraduate or graduate-level students. Researchers or graduate students in systems biology, genomic signal processing, and computer-assisted radiology will find both parts I and ii (on applications) a valuable handbook.

关键词： methods applications contemporary analysis signal biomedical

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