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Brains and phantoms: An ICA study of fMRI

6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006

作者： Ylipaavalniemi, Jarkko Mattila, Seppo Tarkiainen, Antti Vigário, Ricardo Neural Networks Research Centre Laboratory of Computer and Information Science Helsinki University of Technology P.O. Box 5400 FI-02015 TKK Finland Brain Research Unit Low Temperature Laboratory Helsinki University of Technology P.O. Box 2200 FI-02015 TKK Finland Advanced Magnetic Imaging Centre Helsinki University of Technology P.O. Box 3000 FI-02015 TKK Finland

ISBN: (纸本)3540326308

Biomedical signal processing is arguably the most successful application of independent component analysis (ICA) to real world data. For almost a decade, its use in connection with functional magnetic resonance imaging (fMRI) has allowed for data-driven analysis, partly removing the constraints for stringent experimental setups, which are often required by traditional methods based on the use of temporal references. Recent studies on the consistency of independent components have resulted in a series of tools enabling a more reliable use of ICA. In particular, it is now rather easy to detect algorithmic overfitting and isolate subspaces of related activation. Yet, often the nature of the components may not be determined unambiguously. Focal fMRI signals, seemingly originating from within a subject's brain and showing physiologically plausible temporal behavior, are typically considered relevant. This paper presents a study, which makes use of a standard homogeneous spherical phantom and shows evidence for artifacts caused by the measuring device or environment, with characteristics that could easily be misinterpreted as physiological. Our results suggest that reliable analysis of fMRI data using ICA may be far more difficult than previously thought. At least, artificial behavior revealed by phantom analysis should be considered when conclusions are drawn from real subject measurements. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Biomedical engineering

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A comparative study of clustering algorithms

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Information technology Journal 2006年第3期5卷 551-559页

作者： Dehuri, Satchidanandan Mohapatra, Chinmay Ghosh, Ashish Mall, Rajib Department of Information and Communication Technology Fakir Mohan University Vyasa Vihar Balasore India School of Mathematics Statistics and Computer Science Utkal University Vani Vihar India Machine Intelligence Unit Indian Statistical Institute 203 B.T. Road Kolkata-3700108 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur-721302 India

Data clustering is an unsupervised task that can generate different shapes of clusters for a particular type of data set. Hence choosing an algorithm for a particular type of data set is a difficult problem. This study presents the choice of an appropriate clustering algorithm by a comparative study of three representative techniques like K-means, Kohonen's Self Organizing Map (SOM) and Density Based Spatial Clustering of Applications with Noise (DBSCAN) based on the extensive simulation studies. Comparison is performed on the basis of cluster quality index 'β', percentage of samples correctly classified and CPU time. The experimental results show that if the clusters are of arbitrary shape, a density based clustering algorithm like DBSCAN is preferable, where as if the clusters are of hyper spherical or convex shape and well-separated then the SOM or K-means is preferable. © 2006 Asian Network for Scientific Information.

关键词： Algorithms

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Face Recognition Based on Separable Lattice HMMS

Face Recognition Based on Separable Lattice HMMS

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

作者： D. Kurata Y. Nankaku K. Tokuda T. Kitamura Z. Ghahramani Department of Computer Science and Engineering Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Gatsby Computational Neuroscience Unit University College London London UK

In this paper, we propose separable lattice hidden Markov models, in which multiple hidden state sequences interact to model the observation on a lattice. The proposed model can be efficiently applied for modeling images, image sequences, 3-D object models and higher dimensional applications, due to the composite structure of Markov chains which reduces the complexity while retaining good properties for multi-dimensional data. In case of 2-D lattices, the proposed model performs an elastic matching in both horizontal and vertical directions; this makes it possible to model not only invariances to the size and location of an object but also nonlinear warping in each dimension. We present a training algorithm for separable lattice HMMs based on a variational approximation. Moreover, the deterministic annealing EM (DAEM) algorithm was applied to the variational algorithm for separable lattice HMMs. Face recognition experiments on the XM2VTS database show that the proposed model has good properties for face image modeling

关键词： Face recognition Lattices Hidden Markov models Image sequences Maximum likelihood estimation Approximation algorithms Computational complexity computer science Educational institutions Annealing

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A Watershed Based Segmentation Method for Multispectral Chromosome Images Classification

A Watershed Based Segmentation Method for Multispectral Chro...

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

作者： Petros S. Karvelis Dimitrios I. Fotiadis Ioannis Georgiou Marika Syrrou Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece Genetics Unit Department of Obstetrics and Gynecology Medical School Ioannina Greece Genetics Unit Laboratory of General Biology Medical School Ioannina Greece

M-FISH (multicolor fluorescence in situ hybridization) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders which uses 5 fluors to label uniquely each chromosome and a fluorescent DNA stain. In this paper, an automated method for chromosome classification in M-FISH images is presented. The chromosome image is initially decomposed into a set of primitive homogeneous regions through the morphological watershed transform applied to the image intensity gradient magnitude. Each segmented area is then classified using a Bayes classifier. We have evaluated our methodology on a commercial available M-FISH database. The classifier was trained and tested on non-overlapping chromosome images and an overall accuracy of 89% is achieved. By introducing feature averaging on watershed basins, the proposed technique achieves substantially better results than previous methods at a lower computational cost

关键词： Image segmentation Biological cells Image classification Fluorescence Cancer Genetics DNA Image databases Spatial databases Testing

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A general computation rule for lossy summaries/messages with examples from equalization 44

A general computation rule for lossy summaries/messages with...

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44th Annual Allerton Conference on Communication, Control, and Computing 2006

作者： Hu, Junli Loeliger, Hans-Andrea Dauwels, Justin Kschischang, Frank Dept. of Information Technology and Electrical Engineering ETH Zurich ZurichCH-8092 Switzerland Amari Research Unit of the RIKEN Brain Science Institute 2-1 Hirosawa Wako-shi Saitama-ken351-198 Japan Electrical and Computer Engineering Dept University of Toronto ONM4S 3G5 Canada

ISBN: (纸本)9781604237924

Elaborating on prior work by Minka, we formulate a general computation rule for lossy messages. An important special case (with many applications in communications) is the conversion of "soft-bit" messages to Gaussian messages. By this method, the performance of a Kalman equalizer is improved, both for uncoded and coded transmission.

关键词： Equalizers

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A Decision Support System for the Diagnosis of Coronary Artery Disease

A Decision Support System for the Diagnosis of Coronary Arte...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： M.G. Tsipouras T.P. Exarchos D.I. Fotiadis A. Kotsia A. Naka L.K. Michalis Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece Department of Cardiology Medical School University of Ioannina (UoI) Ioannina Greece

A rule-based decision support system is presented for the diagnosis of coronary artery disease. The generation of the decision support system is realized automatically using a three stage methodology: (a) induction of a decision tree from a training set and extraction of a set of rules; (b) transformation of the set of rules into a fuzzy model and (c) optimization of the parameters of the fuzzy model. The system is evaluated using 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Ten fold cross validation was employed and the average sensitivity and specificity obtained was 80% and 65% respectively. Our approach provides diagnosis based on easily acquired features and, since it is rule based, is able to provide interpretation for the decisions made

关键词： Decision support systems Coronary arteriosclerosis Fuzzy sets Fuzzy systems Induction generators Decision trees Data mining Optimization methods Demography History

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A decision tree based approach for the identification of ischaemic beats in ECG recordings

A decision tree based approach for the identification of isc...

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2005 7th International Workshop on Mathematical Methods on Scattering Theory and Biomedical Engineering

作者： Exarchos, T.P. Papaloukas, C. Fotiadis, D.I. Unit of Medical Technology and Intelligent Information Systems Dept. of Computer Science University of Ioannina GR 45110 Ioannina Greece Dept. of Medical Physics Medical School University of Ioannina GR 451 10 Ioannina Greece Dept. of Biological Applications and Technology University of Ioannina GR 45110 Ioannina Greece Biomedical Research Institute - FORTH GR 45110 Ioannina Greece Michaelideion Cardiology Center GR 45110 Ioannina Greece

ISBN: (纸本)9812568603

Cardiac beat classification is a key process in the detection of myocardial ischaemic episodes in the electrocardiographic (ECG) signal. In this, study we propose an automated methodology for the classification of cardiac beats in long duration ECG recordings. The proposed approach is based on the decision tree induction algorithm and consists of three stages. In the first stage the ECG signal is preprocessed and noise removal takes place. Baseline wandering, electromyographic contamination and A/C interference are eliminated. In the second stage five representative features are extracted from every cardiac beat. These features are measurements from the ST segment and the T wave. Another demographic feature, the patient's age is also employed. In the last stage of the method, the decision tree induction algorithm is used. A decision tree is induced from the training set and it is employed in order to classify the test set in two categories: normal and ischaemic cardiac beats. For the evaluation of our methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 92%, respectively. © 2006 World Scientific Publishing Co. Pte. Ltd.

关键词： Decision trees

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An Automated Method for Gridding in Microarray Images

An Automated Method for Gridding in Microarray Images

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

作者： Nikolaos Giannakeas Dimitrios I. Fotiadis Anastasia S. Politou Laboratory of Biological Chemistry Medical School University of Ioannina (UoI) Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece

Microarray technology is a powerful tool for analyzing the expression of a large number of genes in parallel. A typical microarray image consists of a few thousands of spots which determine the level of gene expression in the sample. In this paper we propose a method which automatically addresses each spot area in the image. Initially, a preliminary segmentation of the image is produced using a template matching algorithm. Next, grid and spot finding are realized. The position of non-expressed spots is located and finally a Voronoi diagram is employed to fit the grid on the image. Our method has been evaluated in a set of five images consisting of 45960 spots, from the Stanford microarray database and the reported accuracy for spot detection was 93%

关键词： Image segmentation Biomedical imaging Image analysis Gene expression Fluorescence Image generation Pixel Laboratories Chemistry Cities and towns

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Epileptic Spike Detection Using a Kalman Filter Based Approach

Epileptic Spike Detection Using a Kalman Filter Based Approa...

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

作者： Alexandros T. Tzallas Vaggelis P. Oikonomou Dimitrios I. Fotiadis Department of Medical of Physics Medical School University of Ioannina (UoI) Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece

The electroencephalogram (EEG) consists of an underlying background process with superimposed transient nonstationarities such as epileptic spikes (ESs). The detection of ESs in the EEG is of particular importance in the diagnosis of epilepsy. In this paper a new approach for detecting ESs in EEG recordings is presented. It is based on a time-varying autoregressive model (TVAR) that makes use of the nonstationarities of the EEG signal. The autoregressive (AR) parameters are estimated via Kalman filtering (KF). In our method, the EEG signal is first preprocessed to accentuate ESs and attenuate background activity, and then passed through a thresholding function to determine ES locations. The proposed method is evaluated using simulated signals as well as real inter-ictal EEGs

关键词： Epilepsy Electroencephalography Electronic switching systems Brain modeling Medical diagnostic imaging Parameter estimation Filtering Pathology computer science education Educational programs

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Hierarchical chunking during learning of visuomotor sequences

Hierarchical chunking during learning of visuomotor sequence...

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International Joint Conference on Neural Networks 2006, IJCNN '06

作者： Miyapuram, Krishna P. Bapi, Raju S. Pammi, Chandrasekhar V. S. Ahmed Doya, Kenji Department of Computer and Information Sciences University of Hyderabad Hyderabad 500046 India Department of Physiology Development and Neuroscience Downing Site Cambridge CB2 3DY United Kingdom Department of Computer and Information Sciences Center for Cognitive Science University of Hyderabad Hyderabad 500046 India Department of Psychiatry and Behavioral Sciences Emory University School of Medicine Atlanta GA 30322 United States Computational Intelligence Laboratory Department of Computer and Information Sciences University of Hyderabad Hyderabad 500046 India Department of Computational Neurobiology Computational Neuroscience Laboratories ATR International Kyoto Japan Neural Computation Unit Okinawa Institute of Science and Technology Gushikawa Okinawa Japan

ISBN: (纸本)0780394909

It is well known that learning a sequential skill involves chaining a number of primitive actions together into chunks. We describe three different experiments using an explicit visuomotor sequence learning paradigm called the m×n task. The m×n task enables hierarchical learning of sequences by presenting m elements of the sequence at a time (called the set). The entire sequence to be learned is composed of n such sets and is called a hyperset. In the first experiment, we showed the chunking phenomenon while learning a sequence as opposed to following randomly generated visual cues. We further explored the nature of chunking across sets using complex sequences in the second experiment. Finally, we investigated effector dependence of the chunking patterns in the third experiment. Our results point out the facilitating factors for chunk formation in visuomotor sequence learning. © 2006 IEEE.

关键词： Hierarchical systems

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