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IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering

作者： Sabahi, Hadi Soltanian-Zadeh, Hamid Scarpace, Lisa Mikkelsen, Tom Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI 48202 United States Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

This paper proposes a method to predict the effect of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The prediction is critical for effective treatment planning. The proposed method is developed and evaluated using Diffusion Tensor Imaging (DTI) and post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI) of 14 patients with GBM tumors gathered before and after the treatment. First, the proposed method calculates diffusion anisotropy indices (DAI) of all voxels in the brain. These diffusion anisotropy indices are Fractional Anisotropy (FA), Mean Diffusivity (MD), Relative Anisotropy (RA), and Volume Ratio (VR). Then, it registers post-treatment pc-T1-MRI and pre-treatment DAI maps to pre-treatment pc-T1-MRI. Next, it uses a thresholding method to segment the tumor from pc-T1-MRI studies. Comparing Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI, the DAIs of the tumor are labeled based on their response to the treatment. The voxels of 7 patients are randomly selected to train 4 classifiers (ANN, SVM, KNN, and ANFIS) and then all voxels of the other 7 patients are used to test them. For each classifier, four performance measures (sensitivity, specificity, positive predictive value, and accuracy) are calculated. Experimental results show that the ANFIS is more accurate than the other classifiers in predicting the treatment response. © 2012 IEEE.

关键词： Tumors

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Interpolation of Orientation Distribution Functions (ODFs) in Q-ball Imaging

Interpolation of Orientation Distribution Functions (ODFs) i...

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Iranian Conference of Biomedical Engineering

作者： Maryam Afzali Emad Fatemizadeh Hamid Soltanian-Zadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory(BiSIPL) Sharif University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence(CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran & Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit Michigan USA

ISBN: (纸本)9781467331289

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.

关键词： High angular resolution diffusion imaging (HARDI) Q-ball imaging Orientation distribution function (ODF) Interpolation Principal diffusion direction (PDD)

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Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Using Joint Independent Component Analysis

Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Usin...

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Iranian Conference of Biomedical Engineering

作者： Amir Hosein Riazi Hamid Soltanian-Zadeh Gholam-Ali Hossein-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran & Image Analysis Laboratory Henry Ford Health System Detroit Michigan

ISBN: (纸本)9781467331289

Functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) provide complementary information. Signal processing and statistical models may be used to fuse neuroimaging data across different imaging modalities. In this paper, we present a data driven method for fusing resting state fMRI and diffusion tensor imaging (DTI) data at feature level. The features are amplitude of low frequency fluctuations (ALFF) and fractional anisotropy (FA) extracted from fMRI and DTI datasets of epilepsy and healthy controls, respectively. We discuss main issues associated with group independent component analysis (ICA) as a fusion method. We address our proposed approach for combining two modalities across subjects and back reconstruction of independent components for each group and each subject. Our results indicate that connectivity of regions in default mode network depends on integrity of white matter that connects the two hemispheres (corpus callosum). The proposed signal processing and statistical methods facilitate evaluation of brain connectivity using different modalities. Separate analysis of data modalities does not reveal results of joint analysis.

关键词： FMRI DTI Joint ICA Group Analysis

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A hybrid coherent-incoherent method of modulation filtering for Single Channel Speech Separation

A hybrid coherent-incoherent method of modulation filtering ...

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

作者： A. Mahmoodzadeh H. Sheikhzadeh H. R. Abutalebi H. Soltanian-Zadeh Speech Proc. Research Laboratory ECE Department Yazd University Yazd Iran EE Department Amirkabir University of Technology슠 Tehran Iran Image Analysis Laboratory Henry Ford Health System Detroit USA Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

Single Channel Speech Separation has been the objective of extensive research in recent years. In this paper, we propose a hybrid system of coherent and incoherent modulation filtering for separation of the target speech from the interference. In the proposed system, subband envelopes are determined using a coherently detected subband carrier based on the time-dependent spectral Center-Of-Gravity (COG) demodulation and then the interference signal is eliminated by applying the adaptive Affine Projection (AP) filter to the subband envelop. The reference signal for the adaptive AP filter is provided by a parallel incoherent system of modulation filtering. Our evaluations, based on several objective measures, indicate that the proposed system extracts the majority of target speech signal segments with minimal interference, outperforming previous systems in voiced speech separation.

关键词： Speech Filtering Interference Adaptive filters Frequency modulation Signal to noise ratio

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Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization

Hippocampal shape analysis in the Laplace Beltrami feature s...

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IEEE International Symposium on Biomedical Imaging

作者： Rosita Shishegar Ziba Gandomkar Hamid Soltaman-Zadeh Seyed Reza Moghadasi Control and Intelligent Processing Centre of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Department of Mathematics Sharif University of Technology Tehran Iran

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization accuracy of 86%. The results are comparable to spherical harmonic based shape analysis results and notably improved in comparison to volumetric analysis results.

关键词： Shape Temporal lobe Epilepsy Abstracts Indexes Eigenvalues and eigenfunctions Feature extraction

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Voxel-based treatment prediction of glioblastoma multiform tumor using diffusion tensor imaging

Voxel-based treatment prediction of glioblastoma multiform t...

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

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

关键词： Support vector machines Noise Magnetic resonance imaging Sensitivity Tumors Biomedical imaging Artificial neural networks

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Peripheral region enhancement of geometrically compensated images from a fisheye lens

Peripheral region enhancement of geometrically compensated i...

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1st IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2011

作者： Kim, Donggyun Kim, Seonyung Paik, Joonki Jung, Junghoon Kim, Taechan Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of System LSI Samsung Electronics Co. Ltd. Suwon Korea Republic of

ISBN: (纸本)9781457702341

In this paper, we propose a peripheral region enhancement method for geometrically compensated images from a fisheye lens. The proposed method provides a novel filtering method based on the degradation model of a fisheye lens. More specifically, we first present a space-variant image degradation model using generic Gaussian function with geometric distortion. We then apply the truncated constraint least squares (TCLS) filter for restoring the peripheral region of the image in the real-time, space-adaptive manner. Experimental results demonstrate that the proposed method can enhance the peripheral region while keeping the high-quality center region. © 2011 IEEE.

关键词： image enhancement

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Evaluation of functional connectivity in control and ADHD subjects by generalized canonical correlation and probabilistic independent component analysis

Evaluation of functional connectivity in control and ADHD su...

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2011 18th Iranian Conference of Biomedical Engineering, ICBME 2011

作者： Lashkari, Amirehsan Soltanian-Zadeh, Hamid Hosseinzadeh, Gholamali Elec. and Comp. Eng. Dept. Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9781467310055

Functional connectivity can be evaluated by temporal correlation between spatial neurophysiologic events or correlation between neural activities of brain regions. Unlike anatomical connectivity which represents physical connections between two brain regions, functional connectivity investigates interactions between brain regions. In this study, we implemented and evaluated Probabilistic Independent Component Analysis (PICA) and generalized Canonical Correlation Analysis (gCCA) on resting state data of healthy subjects and subjects with ADHD disorders from NITRC database. This was done to detect brain functional connectivity and compare the results in normal and diseased subjects. In addition, speed of the methods was compared. Our study shows that relative to PICA, gCCA has higher precision and accuracy and lower computational complexity and run time in addition to capability of extracting more reproducible statistical maps in both control and ADHD subjects. © 2011 IEEE.

关键词： Independent component analysis

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Geometrical transformation-based ghost artifacts removing for high dynamic range image

Geometrical transformation-based ghost artifacts removing fo...

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IEEE International Conference on image processing

作者： Jaehyun Im Sangsik Jang Seungwon Lee Joonki Paik Image Processing and Intelligent System Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University South Korea

In this paper, we propose a ghost artifacts removing algorithm for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. Existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, the proposed algorithm first specifies the target and the source images in the set of acquired LDR images, and then the source image is transformed to fit the target image by estimating translation and rotation components in the affine matrix. Therefore, the proposed algorithm can reconstruct the HDR image without ghost artifacts because camera movement is completely removed in transformed images. Experimental results show that the proposed algorithm successfully removes ghost artifacts. For this reason, the proposed algorithm can extend application areas of HDR imaging to various mobile imaging devices, such as a handheld camcorder and a mobile phone camera.

关键词： Cameras Dynamic range Heuristic algorithms image processing Erbium Software

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(Semi-)Analytic Gaussian Mixture Filter

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IFAC Proceedings Volumes 2011年第1期44卷 10014-10020页

作者： Marco F. Huber Frederik Beutler Uwe D. Hanebeck Variable Image Acquisition and Processing Research Group Fraunhofer Institute of Optronics System Technologies and Image Exploitation (IOSB) Karlsruhe Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

Abstract In nonlinear filtering, special types of Gaussian mixture filters are a straightforward extension of Gaussian filters, where linearizing the system model is performed individually for each Gaussian component. In this paper, two novel types of linearization are combined with Gaussian mixture filters. The first linearization is called analytic stochastic linearization, where the linearization is performed analytically and exactly, i.e., without Taylor-series expansion or approximate sample-based density representation. In cases where a full analytical linearization is not possible, the second approach decomposes the nonlinear system into a set of nonlinear subsystems that are conditionally integrable in closed form. These approaches are more accurate than fully applying classical linearization.

关键词： State Estimation Nonlinear systems Stochastic systems Decomposition Methods

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