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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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Automatic segmentation of brain tumors in magnetic resonance images

Automatic segmentation of brain tumors in magnetic resonance...

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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： Behzadfar, Neda Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Pooyandegan Rah Saadat Company Tehran 16756-965 Iran Image Analysis Laboratory Radiology Department HenryFord Healh System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R2=0.97). © 2012 IEEE.

关键词： Tumors

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

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

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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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Development of a MATLAB-Based Toolbox for Brain computer Interface Applications in Virtual Reality

Development of a MATLAB-Based Toolbox for Brain Computer Int...

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Iranian Conference on electrical engineering

作者： Fardin Afdideh Mohammad Bagher Shamsollahi Seyed Navid Resalat Biomedical Signal and Image Processing Laboratory (BiSPL) School of Electrical Engineering Sharif University of Technology Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran

ISBN: (纸本)9781467311496

Brain computer interface (BCI) is a widely used system to assist the disabled and paralyzed people by creating a new communication channel. Among the various methods used in BCI area, motor imagery (MI) is the most popular and the most common one due to its the most natural way of communication for the subject. Some software applications are used to implement BCI systems, and some toolboxes exist for EEG signal processing. In recent years virtual reality (VR) technology has entered into the BCI research area to simulate the real world situations and enhance the subject performance. In this work, a completely MATLAB-based Mi-based BCI system is proposed and implemented in order to navigate into a virtual environment. In addition, a variety of features types were employed to select the best ones in the proposed system with the use of linear discriminant analysis (LDA) classifier through some interactive graphical user interfaces (GUIs). The results show the feasibility of the proposed BCI system in the subject training with or without feedback and even navigation into a virtual home.

关键词： Brain computer Interface (BCI) MATLAB Motor imagery (MI) Virtual Reality (VR) Real-time EEG signal processing

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A non-stationary geometry-based stochastic model for MIMO high-speed train channels

A non-stationary geometry-based stochastic model for MIMO hi...

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International Conference on ITS Telecommunications (ITST)

作者： Ammar Ghazal Cheng-Xiang Wang Harald Haas Mark Beach Raed Mesleh Dongfeng Yuan Xiaohu Ge Mohamed Khaled Chahine Joint Research Institute for Signal and Image Processing Heriot-Watt University Edinburgh UK School of Information Science and Engineering Shandong University Jinan China Joint Research Institute for Signal and Image Processing University of Edinburgh Edinburgh UK Department of Electrical & Electronic Engineering University of Bristol Bristol UK Sensor Networks and Cellular Systems (SNCS) Research Center University of Tabuk Tabuk Saudi Arabia Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan Hubei China Faculty of Mechanical and Electrical Engineering Damascus University Syria

In this paper, a non-stationary wideband geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) high-speed train (HST) channels. The proposed model employs multiple confocal ellipses model, where the received signal is a superposition of the line-of-sight (LoS) and single-bounced rays. Because of the time-varying feature of angles of arrival (AoAs), angles of departure (AoDs), and LoS angle, the proposed GBSM has the ability to investigate the non-stationarity of HST environment caused by the high speed movement of the receiver. From the proposed model, the local spatial cross-correlation function (CCF) and the local temporal autocorrelation (ACF) are derived for different taps. Numerical results and analysis show that the proposed channel model is capable of characterizing the time-variant HST wireless channel.

关键词： Channel models MIMO Wireless communication Educational institutions Correlation Antennas Rail transportation

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Denoising diffusion-weighted MR magnitude image sequences using low rank and edge constraints

Denoising diffusion-weighted MR magnitude image sequences us...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Lam S. Derin Babacan Justin P. Haldar Norbert Schuff Zhi-Pei Liang Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign USA Signal and Image Processing Institute the Dornsife Cognitive Neuroscience Imaging Center and the Brain and Creativity Institute University of Southern California USA Department of Radiology and Biomedical Imaging VA Medical Center University of California San Francisco USA

ISBN: (纸本)9781457718571

This paper addresses the denoising problem associated with diffusion MR imaging. Building on previous approaches to this problem, this paper presents a new method for joint denoising of a sequence of diffusion-weighted (DW) magnitude images. The proposed method uses a maximum a posteriori (MAP) estimation formulation to incorporate a Rician likelihood (for modeling the noisy magnitude data), a low rank model (for the DW image sequences) and a spatial prior (for imposing joint edge constraints). An efficient algorithm to solve the associated optimization problem is also described. The proposed method has been evaluated using both simulated and experimental diffusion tensor imaging (DTI) data, which yields very encouraging results both qualitatively and quantitatively.

关键词： Noise reduction Noise image edge detection Rician channels Diffusion tensor imaging

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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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Automatic organs' detection in WCE

Automatic organs' detection in WCE

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International Symposium on Artificial Intelligence and signal processing (AISP)

作者： Omid Haji-Maghsoudi Alireza Talebpour Hamid Soltanian-Zadeh Navid Haji-maghsoodi Department of Radiation Medicine Shahid Beheshti University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Hospital Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Mechatronics Sharif University of Tehran Tehran Iran

Wireless capsule endoscopy (WCE) views the entire gastrointestinal (GI) tract. A main problem associated with this novel device is that too many frames must be reviewed by physicians. Thus it is essential to find an automatic and intelligent method to help physicians. One of the problems in WCE is its difficulty to distinguish among different organ's tissues. So, we introduce two novel algorithms which are able to classify main organs (among esophagus, stomach, small bowel and colon) in WCE's frames. In order to obtain our aim, we use statistic features (Haralick features) and non-statistic features (different diagrams and Gabor filter banks), colored features and non-colored features. Our experimental studies indicate good results that are shown in conclusion.

关键词： Level set Medical services Gabor filters Active contours Wireless communication Optical wavelength conversion Esophagus

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7191 LNCS卷 V-VII页

作者： Cichocki, Andrzej Theis, Fabian Yeredor, Arie Zibulevsky, Michael Helmholtz Center Munich Institute of Bioinformatics and Systems Biology Ingolstädter Landstr. 1 85764 Neuherberg Germany Riken Brain Science Institute Laboratory for Advanced Brain Signal Processing 2-1 Hirosawa Wako-shi 351-0198 Saitama Japan Tel-Aviv University School of Electrical Engineering Tel-Aviv 69978 Israel Technion - Israel Institute of Technology Department of Computer Science Haifa 32000 Israel

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