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Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

Epileptic EEG Signal Classification with ANFIS Based on Harm...

引用

International Conference on Computational Intelligence and Security

作者： Jing Wang X.Z. Gao Jarno M.A. Tanskanen Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

关键词： Electroencephalography Epilepsy Feature extraction Training Pattern classification Adaptive systems Expert systems

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Curvature oriented clustering of sparse motion vector fields

Curvature oriented clustering of sparse motion vector fields

引用

IEEE Southwest Symposium on image Analysis and Interpretation

作者： Alvaro Guevara Christian Conrad Rudolf Mester Section of Systems Neuroscience Technische Universität Dresden Germany Visual Sensorics and Information Processing (VSI) Laboratory J. W. Goethe Universität Frankfurt Germany Computer Vision Laboratory EE Department Linköping University Sweden

We present an approach to unveil the underlying structure of dynamic scenes from a sparse set of local flow measurements. We first estimate those measurements at carefully selected locations, and subsequently group them into a finite set of different dense flow field hypotheses. These flow fields are represented as parametric functional models, and the number of flow models (=clusters) is determined by an information-theory based approach. Methodically, the grouping task is a two-step clustering scheme, whose intra-cluster modeling step exploits prior knowledge on real flow fields by enforcing low curvature, and the individual covariance matrices of the sparse local flow measurements are introduced in a principled way. The method has been tested successfully on both stereo and general motion sequences from the standard Middlebury database.

关键词： Vectors Venus Motion measurement Clustering algorithms Bismuth image segmentation Covariance matrix

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EEG signal classification with super-Dirichlet mixture model

EEG signal classification with super-Dirichlet mixture model

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Zhanyu Ma Zheng-Hua Tan Swati Prasad School of Electrical Engineering Sound and Image Processing Laboratory (SIP) KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems University of Aalborg Aalborg Denmark Center for TeleInFrastruktur (CTIF) University of Aalborg Aalborg Denmark

Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the transient nature of the signals. To improve the classification performance based on the mDWT coefficients, we propose a new classification method by utilizing the nonnegative and sum-to-one properties of the mDWT coefficients. To this end, the distribution of the mDWT coefficients is modeled by the Dirichlet distribution and the distribution of the mDWT coefficients from more than one channels is described by a super-Dirichlet mixture model (SDMM). The Fisher ratio and the generalization error estimation are applied to select relevant channels, respectively. Compared to the state-of-the-art support vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies.

关键词： Electroencephalography Support vector machines Brain modeling Vectors Discrete wavelet transforms Training Error analysis

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Single camera-based full depth map estimation using color shifting property of a multiple color-filter aperture

Single camera-based full depth map estimation using color sh...

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

作者： Seungwon Lee Junghyun Lee Monson H. Hayes Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

A multiple color-filter aperture (MCA) camera can provide depth information as well as color and intensity in the single-camera framework, where the MCA generates misalignment between color channels depending on the distance of a region-of-interest. In this paper, we present a single camera-based estimation of the full depth map using the color shifting property of the MCA. For estimating the color shifting vectors (CSVs) among red, green, and blue color channels, edges are extracted at each color channel. At the edge, we estimate CSVs using normalized cross correlation combined with color shifting mask map. A full depth map is then generated by depth interpolation using the matting Laplacian method from sparsely estimated CSVs at an edge location. Experimental results show that the proposed method can not only estimate the full depth map but also correct the misaligned color image to generate photorealistic color images using a single camera equipped with MCA.

关键词： image color analysis Cameras Estimation Channel estimation image edge detection Interpolation Apertures

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3D computational modeling of epiretinal prosthesis stimulation of the ganglion cell

3D computational modeling of epiretinal prosthesis stimulati...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shabnam Novin Hamid Abrishami Moghaddam Amir Masoud Sodagar Amin Mahnam Research Laboratory for Integrated Circuits and Systems (I CAS) Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Research Laboratory for Machine Vision and Medical Image Processing (MVMIP) Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran Ecole Polytechnique de Montreal Montreal QUE Canada Department of Biomedical Engineering School of Engineering University of Isfahan Isfahan Iran

Here we present a modeling for electrical stimulation of retinal ganglion cell. This modeling can be used in epiretinal prosthesis to come up with proper electrical stimulation parameters. The model that we present is a novel one based on the anatomy of the cell. The excitation process of the electrically stimulated retinal cells is simulated in a two step procedure. In the first step the ganglion target cell is represented considering all of its compartments and in a second step the extracellular potential along the neural structure is calculated and finally the membrane voltage response to the applied stimulations is analyzed. A monopolar point electrode was positioned above the cell in an infinite and homogenous environment and the monophasic and biphasic pulses were used as stimulating current. The results show that the responses are in accordance with the values and diagrams of experimental results more than the previous models.

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Evaluation of a Novel Wafer-Scale CMOS APS X-Ray Detector for Use in Mammography

Evaluation of a Novel Wafer-Scale CMOS APS X-Ray Detector fo...

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IEEE Nuclear Science Symposium and Medical Imaging Conference

作者： Anastasios C. Konstantinidis Yi Zheng Alessandro Olivo Kristina Bliznakova Mary Yip Thalis Anaxagoras Kevin Wells Nigel Allinson Robert D. Speller Department of Medical Physics and Bioengineering University College London London UK Department of Electronic Engineering and Microelectronics Technical University of Varna Varna Bulgaria Center for Vision Speech and Signal Processing Faculty of Engineering and Physical Sciences University of Surrey Guildford UK Image Engineering Laboratory University of Lincoln Lincoln UK

The most important factors that affect the image quality are contrast, spatial resolution and noise. These factors and their relationship are quantitatively described by the Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio (SNR), Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and Detective Quantum Efficiency (DQE) parameters. The combination of SNR, MTF and NPS determines the DQE, which represents the ability to visualize object details of a certain size and contrast at a given dose. In this study the performance of a novel large area Complementary Metal-Oxide-Semiconductor (CMOS) Active Pixel Sensor (APS) X-ray detector, called DynAMITe (Dynamic range Adjustable for Medical Imaging Technology), was investigated and compared to other three digital mammography systems (namely a) Large Area Sensor (LAS), b) Hamamatsu C9732DK, and c) Anrad SMAM), in terms of physical characteristics and evaluation of the image quality. DynAMITe detector consists of two geometrically superimposed grids: a) 2560 x 2624 pixels at 50 μm pitch, named Sub-Pixels (SP camera) and b) 1280 x 1312 pixels at 100 μm pitch, named Pixels (P camera). The X-ray performance evaluation of DynAMITe SP detector demonstrated high DQE results (0.58 to 0.64 at 0.5 lp/mm). image simulation based on the X-ray performance of the detectors was used to predict and compare the mammographic image quality using ideal software phantoms: a) one representing two three dimensional (3-D) breasts of various thickness and glandularity to estimate the CNR between simulated microcalcifications and the background, and b) the CDMAM 3.4 test tool for a contrast-detail analysis of small thickness and low contrast objects. The results show that DynAMITe SP detector results in high CNR and contrast-detail performance.

关键词： CNR CMOS NPS

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Autocalibration: Finding infinity in a projective reconstruction

Autocalibration: Finding infinity in a projective reconstruc...

引用

Canadian Conference on Computer and Robot vision

作者： Cavan, Neil Fieguth, Paul Clausi, David A. Vision and Image Processing Lab. Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (纸本)9780769543628

In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associated with auto calibration is the nonlinear optimization of a cost function based on extracting camera intrinsics from a potential upgrading transform, and evaluating fitness with respect to prior knowledge of physical cameras. The nonlinearity of the problem leads, in general, to poor convergence and a failure of the calibration process. This paper presents a novel auto calibration pipeline that seeks to develop a more robust approach to the nonlinear optimization. After testing a variety of methods, none of which yielded satisfactory solutions, we have developed a strategy combining the best aspects of two methods representing the current state of the art. The former method preconditions the projective space by ensuring it is quasi-affine with respect to camera centers, allowing a naive initialization in the new space, and uses a fitness measure resistant to focal length collapse. The latter method initializes using the best results of an exhaustive search over reasonable values of focal length. Our novel approach, presented here, uses the exhaustive search initialization of the latter combined with the improved fitness measure of the former, producing results that outperform both of its predecessors. © 2011 IEEE.

关键词： Cameras

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Epileptic EEG signal classification with marching pursuit based on harmony search method

Epileptic EEG signal classification with marching pursuit ba...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ping Guo Jing Wang X. Z. Gao Jarno M. A. Tanskanen Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China School of Foundational Education Peking University Health Science Center China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Department of Biomedical Engineering Tampere University of Technology Finland

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

关键词： Feature extraction Electroencephalography Dictionaries Classification algorithms Training Matching pursuit algorithms Accuracy

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Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Elastic shape registration using an incremental free form de...

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Canadian Conference on Computer and Robot vision

作者： Abdelmunim, Hossam Farag, Aly A. Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo 11517 Egypt Computer Vision and Image Processing Laboratory Electrical and Computer Engineering Department University of Louisville KY 40292 United States

ISBN: (纸本)9780769543628

Shape registration is one of the most challenging problems in computer vision and medical imaging. The process is affected by the way the shape is represented and the form of transformation used to move the source shape. Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as a criteria to establish point correspondences in each level of the IFFD framework. The free form deformation (FFD) is well known in the literature and works by forming a lattice of control points that can move and hence deform the domain grid points smoothly and uniformly under some shape constrains. The control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems. © 2011 IEEE.

关键词： Medical imaging

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Probabilistic shape-based segmentation using level sets

Probabilistic shape-based segmentation using level sets

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2011 IEEE International Conference on Computer vision Workshops, ICCV Workshops 2011

作者： Aslan, Melih S. Abdelmunim, Hossam Farag, Aly A. Computer Vision and Image Processing Lab University of Louisville Louisville KY 40299 United States Computer and Systems Engineering Department Ain Shams University Cairo Egypt

ISBN: (纸本)9781467300629

In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation of the training shapes. The resulting probability density function is used to embed the shape model into the image domain with a new energy minimization solution. Our method' s invariance to parameter initialization is evaluated through validation, and various synthetic and clinical shape registration examples are implemented. Experiments show that our proposed algorithm enhances the conventional global registration results, overcomes segmentation challenges, and is robust under various noise levels, severe occlusions, and missing parts. © 2011 IEEE.

关键词： Probability density function

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