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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

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.

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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Numerical Solutions to the Bloch-McConnell Equations with Radio Frequency Irradiation Scheme for CEST MRI

Numerical Solutions to the Bloch-McConnell Equations with Ra...

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Iranian Conference on Electrical Engineering

作者： Mohammadreza Rezaeian Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zaseh Control and Intelligent Processing Center of Excellence CIPCE School of ECE College of Eng. Image Analysis Laboratory Radiology Department Henry Ford Hospital Sheikhbahaee University Faculty of Engineering

ISBN: (纸本)9781467311496

Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is complex and depends on radio frequency (RF) pulses used in experimental conditions. To find the optimal RF pulse, numerical solutions of Bloch-McConnell equations may be used. The purpose of this work is to compare the effects of different RF pulses on the CEST phenomenon. First, a two-pool exchange model for CEST in MRI is used. Z-spectra and signal calculations for the two-pool model have been carried out for some RF pulses. Efficiency of both continuous and binomial RF pulses is investigated by calculating three parameters, namely: CEST image contrast, flip angle and specific absorption ratio (SAR). The contrast of CEST image is considered proportional to amount of saturation. As a binomial RF pulse, WALTZ-16* demonstrates a good efficiency, but generates some unwanted direct saturations. On the other hand, continuous RF pulses do not create any unwanted direct saturation, but have less efficiency.

关键词： Bloch equations Bloch-McConnell equations CEST MRI Numerical solution SAR

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Effective supervised classification of fMRI activation maps between populations by spatial descriptors

Effective supervised classification of fMRI activation maps ...

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2011 7th Iranian Conference on Machine Vision and image processing, MVIP 2011

作者： Eshaghian, Shaghayegh Hossein-Zadeh, Gholam-Ali Soltanian-Zadeh, Hamid 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

ISBN: (纸本)9781457715358

The major obstacle in discrimination between different groups of subjects in a common cognitive state, by functional Magnetic Resonance Imaging (fMRI), has been the high intersubject functional and anatomical variability in the spatial patterns of brain activity. To overcome this, we have used two types of spatial descriptors that characterize the brain regions of interest (ROIs) involved in the cognitive tasks. They include, firstly three-dimensional invariant moment descriptors (3-DMIs), and secondly k-dimensional feature vectors based on concentric spheres. Both types of descriptors are applied to analyze the spatial patterns of cognitive activity of a challenging task and then to classify them across two different subject groups. SVM classifiers along with sequential floating forward feature selection technique are applied to the extracted descriptors of each ROI across the subjects. Our method is applied to experimental fMRI data with the aim of discriminating mental status of heroin IV (Intravenous) abusers and from of those in control subjects in a visual cue task which can induce drug craving. Our results demonstrate that 3-D texture of activation maps provide a good discrimination (with high accuracy) between healthy and addict group. © 2011 IEEE.

关键词： Brain

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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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Superiority of the 3rd order approximation model to the 2nd order in Diffusion Kurtosis Imaging

Superiority of the 3rd order approximation model to the 2nd ...

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作者： Iraji, Armin Davoodi-Bojd, Esmaeil Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam-Ali 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

ISBN: (纸本)9789644634284

For estimating the kurtosis parameter in Diffusion Kurtosis Imaging (DKI), usually second order expansion of the diffusion signal is used in conventional acquisition data. However, in this work, we show that this is not sufficient and should be reconsidered. Instead of using the second order expansion, we propose to use the third order expansion. At first, we show that the third order term of the Taylor's expansion has considerable values compared to second and first terms for the conventional b-values used in DKI. Then, for proving our claim empirically, we use real data to compare the proposed model with the previous one. To this end, five regions of the brain are extracted and for each region the fitting errors of both models are computed. The results confirm that the fitting error of the third order model is always (for all regions) less than the second order model. Although the improvement of the fitting error seems to be small, but considering the sensitivity analysis, this improvement can enhance the estimation of kurtosis. The resulting maps of the kurtosis parameter show this enhancement. The white matter in the map of third order model is more distinguishable than in the other model. © 2011 AmirKabir Univ of Tech.

关键词： Diffusion

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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2011 18th Iranian Conference of Biomedical Engineering, ICBME 2011

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)9781467310055

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures. © 2011 IEEE.

关键词： Tumors

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