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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2012年第2期39卷 155-164页

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Faculty of Electrical Engineering Computer Vision Video and Image Processing Research Lab Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

This paper presents a fully automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI). The lesions are infarction, hemorrhage, tumor and abscess. Pre-processing stage is performed for intensity normalization, background removal and intensity enhancement. Then, split and merge algorithm is designed. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. Lesions are segmented by merging the homogenous regions according to the selected criteria. This process produces blocky lesion region. Then, histogram thresholding is acquired to automate the seeds selection for region growing process. The region is iteratively grown by comparing all unallocated neighboring pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. The proposed segmentation technique has been validated by using misclassified area (MA), false positive rate (FPR), false negative rate (FNR), mean absolute percentage error (MAPE) and pixel absolute error ratio (rerr), and compared with previous methods. The result shows that automatic region growing method can successfully segment the lesions and is suitable for analysis and classification of DWI.

关键词： Magnetic resonance imaging

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Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm

Brain tissue segmentation using an unsupervised clustering t...

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17th Iranian Conference in Biomedical engineering, ICBME 2010

作者： Azarbad, Milad Ebrahimzadeh, Ataollah Babajani-Feremi, Abbas Faculty of Electrical and Computer Engineering BABOL University of Technology Babol Iran Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit United States

ISBN: (纸本)9781424474844

image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%. © 2010 IEEE.

关键词： Magnetic resonance

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A statistical shape model using 2D-principal component analysis from few medical samples and its evaluation

A statistical shape model using 2D-principal component analy...

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2nd International Conference on Software engineering and Data Mining, SEDM 2010

作者： Tateyama, Tomoko Tanaka, Taishi Kohara, Shinya Foruzan, Amir Hossein Furukawa, Akira Chen, Yen-Wei Intelligent Image Processing Lab College of Information Science and Engineering Ritsumeikan University Shiga Japan Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Shiga University of Medical Science Shiga Japan

ISBN: (纸本)9788988678213

Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.

关键词： Principal component analysis

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Car Plate Character Extraction and Recognition Using Stroke analysis

Car Plate Character Extraction and Recognition Using Stroke ...

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International IEEE Conference on Signal-image Technologies and Internet-Based System

作者： S. Setumin U.U. Sheikh S.A.R Abu-Bakar Computer Vision Video and Image Processing Lab Department of Microelectronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

In this paper we address the issue of recognizing nonstandard Malaysian car license plates. These plates contain nonstandard characters such as italic, cursive and connected letters, which most plate recognition systems are unable to recognize. We propose a technique using stroke extraction and analysis to recognize these nonstandard characters. The proposed technique first extracts the contour of the character and then the stroke direction which are used for classification. The advantage of this method is that the system requires no training. From the experiments performed, the method has a correct recognition accuracy of 95% and even works with standard car plates.

关键词： Pixel Character recognition Classification algorithms Licenses Artificial neural networks Training Algorithm design and analysis

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Character-based car plate detection and localization

Character-based car plate detection and localization

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International Symposium on Signal processing and Its Applications (ISSPA)

作者： S. Setumin U.U. Sheikh S.A.R Abu-Bakar Computer Vision Video and Image Processing Research Lab Department of Microelectronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

In this paper we address the issue of locating non-standard Malaysian car license plate. Instead of searching the region for the plate, we directly locate the alphanumeric characters of the car plate. In this manner, we remove issues such as plate size variations and plates on black colored vehicles. Our main goal is to locate and extract the alphanumeric characters of Malaysian special plates. These special plates do not follow the normal standard car plates' format as they may contain italic, cursive and connected letters, and of different fonts. Using several parameters such as pixel compactness, angles, and projection histogram, we use ruled-based technique to locate and detect these special characters of the car plates. The results have shown that we are able to automatically locate with an accuracy of 95%.

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Classification of hyperspectral images by using morphological attribute filters and independent component analysis

Classification of hyperspectral images by using morphologica...

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Workshop on Hyperspectral image and Signal processing

作者： Mura, Mauro Dalla Villa, Alberto Benediktsson, Jon Atli Chanussot, Jocelyn Bruzzonea, Lorenzo Department of Information Engineering and Computer Science University of Trento Via Sommarive 14 I-38123 Povo Trento Italy Faculty of Electrical and Computer Engineering University of Iceland Hjardarhaga 2-6 101 Reykjavik Iceland GIPSA-lab Signal and Image Dept. Grenoble Institute of Technology - INP 961 rue de la Houille Blanche 38402 Grenoble Cedex France

ISBN: (纸本)9781424489077

In this paper, a technique based on Independent Component analysis (ICA) and morphological attribute filters is presented for the classification of high geometrical resolution hyperspectral images. The ICA is computed instead of the conventional principal component analysis (PCA) in order to better model the information in the hyperspectral image. The spatial characteristics of the objects in the scene are modeled by different multi-level attribute filters. Moreover, a method for increasing the robustness of the analysis based on a decision fusion strategy is proposed. A hyperspectral high resolution image acquired over the city of Pavia (Italy) was considered in the experiments. ©2010 IEEE.

关键词： Independent component analysis

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Evaluation of liver shape approximation and characterization

Evaluation of liver shape approximation and characterization

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IIH-MSP 2009 - 2009 5th International Conference on Intelligent information Hiding and Multimedia Signal processing

作者： Mofrad, Farshid Babapour Zoroofi, Reza Aghaeizadeh Chen, Yen-Wei Tehrani-Fard, Ali Abbaspour Sato, Yoshinobu Furukawa, Akira College of Information and Science Ristumeikan University Shiga Japan Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electrical and Electronic Engineering School Sharif University of Technology Tehran Iran Division of Image Analysis Graduate School of Medicine Osaka University Osaka Japan Department of Radiology Shiga University of Medical Science Shiga Japan

ISBN: (纸本)9780769537627

A computational framework is presented for 3-D liver shape approximation and characterization in order to determine the accuracy of shape reconstruction via Spherical Harmonics expansion. Spherical Harmonics is a powerful mathematical tool for expanding the shape. But in medical domain, livers have very variation geometry, in shape, size, and volume. In this regards, we evaluated and optimized the Spherical Harmonics to create 3-D parametric surface of the liver from Computed Tomography (CT) imaging system which may useful for Shape modeling, surface representation, physical measurement of objects and mathematical model. We select randomly 5 livers from more 100 dataset. Mean Hausdorff distance Errors and Dice similarity coefficient DSC by liver volumes was measured. The experimental results showed that the best order of Spherical Harmonics Expansion for livers is between 12∼17. © 2009 IEEE.

关键词： Harmonic analysis

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Eccentric error and compensation in rotationally symmetric laser triangulation

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仪器仪表学报 2007年第9期28卷 1548-1553页

作者： Wang Lei Gao Jun Wang Xiaojia Johannes Eckstein Peter Ott Lab of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Optical Engineering Lab. Heilbronn University Heilbronn D-74081 Germany Dep. of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China

Rotationally symmetric triangulation(RST)sensor has more flexibility and less uncertainty limits because of the abaxial rotationally symmetric optical *** if the incident laser is eccentric,the symmetry of the image will descend,and it will result in the eccentric error especially when some part of the imaged ring is *** model of rotationally symmetric triangulation that meets the Schimpflug condition is presented in this *** error from eccentric incident laser is *** is pointed out that the eccentric error is composed of two parts,one is a cosine in circumference and proportional to the eccentric departure factor,and the other is a much smaller quadric factor of the *** the ring is complete,the first error factor is zero because it is integrated in whole ring, but if some part of the ring is blocked,the first factor will be the main *** verifies the result of the a- *** last,a compensation method to the error when some part of the ring is lost is presented based on neural *** results of experiment show that the compensation will make the absolute maximum error descend to half,and the standard deviation of error descends to 1/3.

关键词：补偿作用旋转对称激光三角测量法偏心因子视力测量神经网络

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A Hierarchical Clustering Based on Mutual information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on image processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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