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Sparse differential connectivity graph of scalp EEG for epileptic patients

Sparse differential connectivity graph of scalp EEG for epil...

引用

17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009

作者： Amini, L. Achard, S. Jutten, C. Soltanian-Zadeh, H. Hossein-Zadeh, G.A. David, O. Vercueil, L. INPG Domaine universitaire- BP 46 F-38402 Grenoble Cedex France Electrical and Computer Engineering Department University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States U836 38043 Grenoble France

ISBN: (纸本)2930307099

The aim of the work is to integrate the information modulation of the inter-relations between EEG scalp measurements of two brain states in a connectivity graph. We present a sparse differential connectivity graph (SDCG) to distinguish the effectively modulated connections between epileptiform and non-epileptiform states of the brain from all the common connections created by noise, artifact, unwanted background activities and their related volume conduction effect. The proposed method is applied on real epileptic EEG data. Clustering the extracted features from SDCG may present valuable information about the epileptiform focus and their relations.

关键词： Brain

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

引用

Congress on image and Signal Processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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Automatically identifying white matter tracts using cortical labels

Automatically identifying white matter tracts using cortical...

引用

IEEE International Symposium on Biomedical Imaging

作者： John A. Bogovic Aaron Carass Jing Wan Bennett A. Landman Jerry L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Johns Hopkins University USA

Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health of white matter in the brain. Tractography, the tracing of individual fibers in the brain using DTI data, has begun to play a more central role in neuroscience research, particularly in understanding the relationships between brain connectivity and behavior. The measuring of features related to bundles of fibers, i.e., tracts or fasciculi, is currently problematic because of the need for manual interaction. This article presents an algorithm for the automatic identification of selected white matter tracts. It extracts fibers using the FACT algorithm and finds cortical gy- ral labels using a multi-atlas deformable registration scheme. Tracts are identified as the fibers passing between selected cortical labels. The quality of automatic labels are compared both visually and numerically against a well-accepted manual approach. The automatic approach is shown to be more consistent with conventional definitions of tracts and more re- peatable on separate scans of the same subject.

关键词： Diffusion tensor imaging Magnetic resonance imaging Tensile stress image reconstruction Labeling Voting image analysis Laboratories Communication standards Optical fiber communication

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Fuzzy c-means with variable compactness

Fuzzy c-means with variable compactness

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IEEE International Symposium on Biomedical Imaging

作者： Snehashis Roy Harsh Agarwal Aaron Carass Ying Bai Dzung L. Pham Jerry L. Prince Electrical and Computer Engineering Image Analysis and Communications Laboratory MedIC Neuroradiology Division Radiology and Radiological Science

Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.

关键词： image segmentation Biomedical imaging Magnetic resonance Data acquisition Brain Clustering algorithms Magnetic resonance imaging Pixel image sequence analysis Laboratories

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MR artifact reduction in the simultaneous acquisition of EEG and fMRI of epileptic patients

MR artifact reduction in the simultaneous acquisition of EEG...

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16th European Signal Processing Conference, EUSIPCO 2008

作者： Amini, L. Sameni, R. Jutten, C. Hossein-Zadeh, G.A. Soltanian-Zadeh, H. GIPSA-LAB INPG 46 Avenue Félix Viallet 38031 Grenoble cedex France Electrical and Computer Engineering Department College of Engineering University of Tehran Tehran Iran School of Electrical Engineering Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Hospital Detroit MI 48202 United States

Integrating high spatial resolution of functional magnetic resonance imaging (fMRI) and high temporal resolution of electroencephalogram (EEG) is promising in simultaneous EEG and fMRI analysis, especially for epileptic patients. The EEG recorded inside an MR scanner is interfered with MR artifacts. In this article, we propose new artifact reduction approaches and compare them with the conventional artifact reduction methods. Our proposed approaches are based on generalized eigenvalue decomposition (GEVD) and median filtering. The proposed methods are applied on experimental simultaneous EEG and fMRI recordings of an epileptic patient. The results show significant improvement over conventional MR artifact reduction methods. copyright by EURASIP.

关键词： Electroencephalography

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Automated reliable labeling of the cortical surface

Automated reliable labeling of the cortical surface

引用

IEEE International Symposium on Biomedical Imaging

作者： Jing Wan Aaron Carass Susan M. Resnick Jerry L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Johns Hopkins University USA National Institute Aging National Institutes of Health DHHS USA

ISBN: (纸本)9781424420025

Accurate and reliable labeling of regions of interest (ROIs) within structural magnetic resonance images (MRIs) is an important step in group analysis. Group registration does not always yield accurate alignment of homologous regions. We present an approach that distinguishes itself from other algorithms by being concerned with a label which is of the highest fidelity, while leaving ambiguous regions unlabeled. Regions that are not deemed to be reliably labeled are not included in group statistics. We will present results showing that our method is an improvement over traditional multi-atlas voting schemes. We conclude with a pilot study of longitudinal trends of cortical thickness in normal aging.

关键词： Labeling Aging image analysis Voting Brain Laboratories Reliability engineering Magnetic resonance Magnetic resonance imaging Magnetic analysis

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Color Effect on the Face Recognition with Spatial Resolution Constraints

Color Effect on the Face Recognition with Spatial Resolution...

引用

IEEE International Symposium on Multimedia

作者： Jae Young Choi Seungji Yang Yong Man Ro Konstantinos N. Plataniotis Image and Video System Laboratory Information and Communications University South Korea Department of Electrical and Computer Engineering University of Toronto Canada

In the practical face recognition (FR) applications, low-resolution faces (20 times 20 pixels or less) are commonly encountered and negatively impact on reliable performance. To overcome low-resolution face problem, we show that face color can significantly improve the performance compared to intensity-based features. The contribution of this paper is twofold. First, a new metric called dasiavariation ratio gainpsila (VRG) is proposed to theoretically prove the significance of color effect on low-resolution faces. Second, we conduct extensive performance comparison studies. In particular, 3,192 color facial images corresponding to 341 subjects, collected from three standard CMU PIE, FERET, and XM2VTSDB face databases, were used to perform comparative studies of color effect on various face resolutions. Experimental results verified that face color feature improves the degraded recognition rate due to low-resolution faces by at least an order of magnitude over intensity-based features.

关键词： Face recognition Spatial resolution Face detection image color analysis Gray-scale Reluctance generators Robustness Degradation Principal component analysis Linear discriminant analysis

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Voxel-Based Morphometric Study of Brain Regions from Magnetic Resonance images in Temporal Lobe Epilepsy

Voxel-Based Morphometric Study of Brain Regions from Magneti...

引用

IEEE Southwest Symposium on image analysis and Interpretation

作者： Matineh Shaker Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

ISBN: (纸本)9781424422968;1424422965

The aim of this study was to analyze the brain structures in temporal lobe epilepsy (TLE) using 3D T1-weighed magnetic resonance images (MRI). We used a method of voxel-based morphometry (VBM) with the unified segmentation framework to examine the effects of TLE on specific brain structures in 22 patients. Also, 18 healthy control subjects were included in this study for group comparison. We accomplished GM segmentation, spatial normalization, and intensity non-uniformity correction in the same model. Besides, removal of nonbrain voxels and compensation of spatial normalization effects were performed in this study. Student's t-test statistical models of differences between gray matter concentration (GMC) of patients and controls were obtained using a general linear model (GLM). Compared to controls, TLE presented GMC reduction in hippocampus, amygdala, and entorhinal cortex. Comparison of abnormal areas detected by VBM method and manual delineation of these structures confirms the obtained results.

关键词： Magnetic resonance Temporal lobe Epilepsy image segmentation Magnetic resonance imaging Hippocampus image analysis Magnetic analysis Brain modeling Diseases

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Non-ideal iris segmentation using graph cuts

Non-ideal iris segmentation using graph cuts

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Shrinivas J. Pundlik Damon L. Woodard Stanley T. Birchfield Electrical and Computer Engineering Department Clemson University Clemson SC USA School of Computing Image and Video Analysis Laboratory Clemson SC USA

A non-ideal iris segmentation approach using graph cuts is presented. Unlike many existing algorithms for iris localization which extensively utilize eye geometry, the proposed approach is predominantly based on image intensities. In a step-wise procedure, first eyelashes are segmented from the input images using image texture, then the iris is segmented using grayscale information, followed by a post-processing step that utilizes eye geometry to refine the results. A preprocessing step removes specular reflections in the iris, and image gradients in a pixel neighborhood are used to compute texture. The image is modeled as a Markov random field, and a graph cut based energy minimization algorithm [2] is used to separate textured and untextured regions for eyelash segmentation, as well as to segment the pupil, iris, and background using pixel intensity values. The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images. A comparison of the estimated iris region parameters with the ground truth data is provided.

关键词： Iris image segmentation Eyelashes Pixel image texture Gray-scale Information geometry Reflection Markov random fields Minimization methods

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Feature subspace determination in video-based mismatched face recognition

Feature subspace determination in video-based mismatched fac...

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International Conference on Automatic Face and Gesture Recognition

作者： Jae Young Choi Yong Man Ro Konstantinos N. Plataniotis Image and Video System Laboratory Information and Communications University South Korea The Edward S Rogers Sr Department of Electrical and Computer Engineering University of Toronto Canada

In video-based face recognition (FR) applications such as surveillance security, the resolution of a facial image could significantly impact the reliability of recognition system. In most practical FR applications, it is reasonable to assume that the face resolution used during training is higher than the resolution used during the identification or verification process. This dimensional mismatch negatively impacts the performance of the traditional subspace recognition solutions. To address resolution mismatch problem, this paper introduces a novel estimation method capable of determining feature subspace of the lower resolution probe given an eigenspace pre-trained with higher resolution facial images. The effectiveness of the proposed solution has been successfully tested on standard CMU PIE face dataset. Experimental results and comparative evaluations provided in this work demonstrate the benefits of the proposed solution.

关键词： Face recognition image resolution Probes Face detection Information security Application software Surveillance image recognition Laboratories Strontium

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