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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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A Novel Local Spline Smoothing-Based Approach for Medical Ultrasound Images Enhancement

A Novel Local Spline Smoothing-Based Approach for Medical Ul...

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IEEE-EMBS Conference on Biomedical Engineering and Sciences

作者： Fardin Afdideh Roya Khasheei Rad Seyed Navid Resalat Elyas Sabeti IEEE Member Biomedical Engineering Department Islamic Azad University Control and Intelligent Processing Center of Excellence University of Tehran Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology

ISBN: (纸本)9781467316644

In this work, a novel local spline smoothing-based approach is proposed in order to despeckling medical ultrasound images. For better comparison, firstly nine conventional statistical spatial filters were applied on the ultrasound images and the results evaluated by quantitative indices of contrast to noise ratio (CNR) and speckle signal to noise ratio (SSNR). Secondly, one-dimensional cubic spline smoothing (1DCSS) function was applied on the whole speckled image as the first proposed approach. Finally, as the second proposed approach, 1DCSS was applied locally on the sliding windows of the ultrasound image then conventional statistical spatial filters were applied. The results indicate that the proposed approaches outperform the existing conventional ones.

关键词： Cubic spline smoothing Despeckling Medical ultrasound image Spatial filters

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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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Smart plugs for building energy management systems

Smart plugs for building energy management systems

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Iranian Conference on Smart Grids (ICSG)

作者： Hamed Morsali Seyed Mohammad Shekarabi Kamyar Ardekani Hossein Khayami Alireza Fereidunian Mona Ghassemian Hamid Lesani Systems and Machines Research Laboratory (SMRL) Control and Intelligent Processing Center of Excellence (CIPCE) School of ECE University of Tehran Tehran Iran Faculty of ECE K.N. Toosi University of Technology Tehran Iran School of Computing and Mathematical Sciences University of Greenwich London UK

Due to rising value of energy supplies, the need for managing electrical energy sources becomes prominent. In this paper, a smart plug device is presented; which provides features to measure energy consumption and recognizes the type of attached electrical devices. Furthermore, the proposed framework enables home controller systems to monitor and control home energy consumption efficiently. Subsequently an analysis of energy consumption of a pilot house, prior and after the installation of the smart plug, based on a hypothetical scenario, is provided. The results present positive impacts of using the smart plug on energy consumption rate; especially during peak hours.

关键词： Plugs Home appliances Energy consumption Switches Monitoring Energy management

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Performance evaluation for DNO governance using data envelopment analysis method

Performance evaluation for DNO governance using data envelop...

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Iranian Conference on Smart Grids (ICSG)

作者： Amin Ghafouri Alireza Fereidunian Hamid Lesani Habib Torabi Payam Kharazmi Systems and Machines Research Laboratory (SMRL) Control and Intelligent Processing Center of Excellence CIPCE) School of ECE University of Tehran Tehran Iran Smart Grid Research Laboratory Faculty of ECE K.N. Toosi University of Technology Tehran Iran The Greater Tehran Electricity Distribution Company (GTEDC) Tehran Iran

Smart performance evaluation of Distribution Network Operators (DNOs) is an essential part of their effective governance task. This paper focuses on benchmarking twenty one districts of the Greater Tehran Electricity Distribution Company (GTEDC) using Data Envelopment Analysis (DEA). Three methods are employed, namely: CCR (Charnes, Cooper, and Rhodes), SBM (Slack Based Measurement), and WSBM (Weighted Slack Based Measurement). The results consist of efficiency scores of CCR, SBM, and WSBM with slacks analysis for the latter. Based on the WSBM results, weak districts are clarified and necessary improvements are suggested.

关键词： Electricity Companies Data envelopment analysis Vectors Benchmark testing Performance evaluation Weight measurement

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High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances

High-speed SSVEP-based BCI: Study of various frequency pairs...

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

作者： Seyed Navid Resalat Valiallah Saba Fardin Afdideh Ali Heidarnejad Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran AJA University of Medical Sciences Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering College of Engineering Sharif University of Technology Tehran Iran

Brain Computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important. In this work, a SSVEP-based BCI with two external stimulants was designed. In order to determine the best twinkling frequency of stimulants and the best distance between them, the classification accuracy for seven different twinkling frequency pairs and five different stimulants distances was calculated. Two methods for feature extraction step were proposed and the Max classifier was used for classification in order to speed up the computational burden of classification step. Features were extracted from four different segment lengths (sweeps). The results showed that nearly in all sweeps and all inter-sources distances the frequency pair of 10-15 Hz has the highest classification accuracy among other frequency pairs, which is 92% in the inter-sources distance of 24 cm and sweep length of 3 seconds. In addition, the results demonstrated that the method 2 feature extraction technique outperforms the method 1. In addition, for determining the best sweep length, Information Transfer Rate (ITR) was computed and the results indicated that the sweep length of 0.5 second has the highest ITR, so would be practical in real-time applications of BCI.

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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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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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Impaired Resting State Networks in Temporal Lobe Epilepsy: A Resting State fMRI Study

Impaired Resting State Networks in Temporal Lobe Epilepsy: A...

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Iranian Conference of Biomedical Engineering

作者： Marzie Fereidouni Hamid Soltanian-Zadeh Quan Jiang Kost V. Elisevich Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran NMR Laboratory Department of Neurology Henry Ford Hospital Detroit Michigan USA epartment of Clinical Neurosciences Spectrum Health System Grand Rapids Michigan USA

ISBN: (纸本)9781467331289

Temporal lobe epilepsy (TLE) is the most common type of human refractory epilepsy and can cause widespread impairments in brain functionality. Previous studies show functional and structural abnormalities in different regions of TLE patients' brain in comparison with normal subjects. However, limited studies have been done on connectivity abnormalities of resting state networks (RSN) in TLE patients and most of them studied default mode network (DMN) only. In this paper, nine independent spatial maps of the brain were identified by applying independent component analysis (ICA) on functional magnetic resonance images (fMRI) of the subjects. These maps were classified into four resting state networks: visual network, auditory network, default mode network, and attention network (including right and left fronto-parietal networks and core network). Functional connectivity between time courses of the nine components were computed and then compared between TLE patients and healthy subjects. Results show that there are significant between-group differences in functional connectivity among different brain networks including decrease in functional connections of visual cortex with two other networks, DMN and attention network, and decrease in functional connectivity between DMN and left fronto-parietal network. An increase in functional connectivity between DMN and core network and also between right fronto-parietal network and auditory network is observed.

关键词： Temporal lobe epilepsy Functional connectivity Resting state networks

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