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Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm

Brain tissue segmentation using an unsupervised clustering t...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Milad Azarbad Ataollah Ebrahimzadeh Abbas Babajani-Feremi Faculty of Electrical and Computer Engineering BABOL University of Technology Babol Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit USA

image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%.

关键词： image segmentation Heating computers

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation analysis (CCA) and the spatial Independent Component analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

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Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Comparison of voxel-based morphometry (VBM) and tractography...

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Iranian Conference on electrical engineering (ICEE)

作者： Maryam Afzali Hamid Soltanian-Zadeh 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 Health System Detroit MI USA

ISBN: (纸本)9781424467600

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE.

关键词： Tensile stress Magnetic resonance imaging Diffusion tensor imaging Temporal lobe Epilepsy Ear Optical fiber testing Diseases Focusing Medical treatment

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Spectral clustering of resting state fMRI reveals default mode network with specifically reduced network homogeneity in major depression

Spectral clustering of resting state fMRI reveals default mo...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Alireza Sojoudi Seyed Mohammad Shams Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Bernhard Bogerts Kolja Schiltz Martin Walter Control & Intelligent Centre of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA Department of Psychiatry University of Magdeburg Magdeburg Germany

Although resting state fMRI seems an ideal tool for investigating clinical populations, especially in case of reduced cooperation or tolerance, unbiased methods with high sensitivity for disease relevant pathologies remain to be identified. In this paper, we perform spectral clustering on the mean time series of automated anatomical labeling regions of interest for comparing the resting state networks in healthy volunteers and major depression disorder (MDD) patients. A new network homogeneity measure is suggested as a criterion for evaluating the level of homogeneity in a network. We found reduced network homogeneity specifically within the default mode network in MDD subjects compared to age-matched controls. In contrast to previously proposed methods investigating network homogeneity, we fully relied on data-driven definition of clusters of interest to fill an important gap between ROI based network analyses and those using ICA.

关键词： Smoothing methods

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Spatio-temporal localization of focal epileptic sources from intracranial electrocortical recordings using an independent component analysis (ICA) algorithm

Spatio-temporal localization of focal epileptic sources from...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mohamed Amin Latif Abbas Babajani-Feremi Daniel Barkmeier Jeffery A. Leob Hamid Soltanian-Zadeh Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA The Center of Molecular Medicine and Genetics Wayne State University School of Medicine Detroit MI USA CIPCE Electrical and Computer Engineering Department University of Tehran Tehran Iran

The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring system to detect and localize these deep sources. The method is based on an independent component analysis (ICA) algorithm known as joint approximate diagonalization of eigen-matrices (JADE). This method was tested on patients with neocortical epileptic foci focusing on the interictal spikes from ECoG signals. The proposed method localizes the interictal epileptic discharges to both superficial as well as deep regions of the human neocortex and has the potential to improve the localization and surgical outcomes of patients with medically refractory epilepsy undergoing surgical resections.

关键词： Biological system modeling Epilepsy Surgery Electrodes Magnetic resonance imaging Signal to noise ratio Brain modeling

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Attribute ranking for lateralizing focal epileptogenicity in temporal lobe epilepsy

Attribute ranking for lateralizing focal epileptogenicity in...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Shobeir Fakhraei Hamid Soltanian-Zadeh Kost Elisevich Farshad Fotouhi Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA CIPCE School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

A consensus feature-ranking approach has been applied to the study of localization-related temporal lobe epilepsy (TLE) in order to evaluate the relative discriminative power of individual attributes. Cases were selected on the basis of a postoperative outcome free of disabling seizures (i.e., Engel class I) in order to establish a definitive laterality of focal epileptogenicity. Several quantitative measures made available by imaging and electrographic studies are considered and the most discriminative of these are quantitatively prioritized for the lateralization of focal epileptogenicity. Cases requiring extraoperative electrocorticography were examined as a subgroup to establish whether the current method of analysis could distinguish laterality sufficiently well to avoid the requirement for intracranial electrode implantation.

关键词： Electroencephalography Medical diagnostic imaging Epilepsy Data mining Temporal lobe

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Consensus Feature Ranking in Datasets with Missing Values

Consensus Feature Ranking in Datasets with Missing Values

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International Conference on Machine Learning and Applications (ICMLA)

作者： Shobeir Fakhraei Hamid Soltanian-Zadeh Farshad Fotouhi Kost Elisevich Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA CIPCE School of Electrical And Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.

关键词： Accuracy Support vector machines Classification algorithms Niobium Medical diagnostic imaging Data mining

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Multiple-atlas-based automatic sementation of hippocampus for lateralization in temporal lobe epilepsy

Multiple-atlas-based automatic sementation of hippocampus fo...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi-Asl Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Image Analysis Laboratory Department of Diagnostic Radiology Henry Ford Health System Detroit MI USA School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424441259

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.

关键词： Hippocampus Temporal lobe Epilepsy image segmentation Principal component analysis Shape control Probability density function Level set Skull Algorithm design and analysis

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Automatic segmentation of brain structures using geometric moment invariants and artificial neural networks

Automatic segmentation of brain structures using geometric m...

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21st International Conference on Information Processing in Medical Imaging, IPMI 2009

作者： Jabarouti Moghaddam, Mostafa Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI United States

ISBN: (纸本)3642024971

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature. © 2009 Springer Berlin Heidelberg.

关键词： Neural networks

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A Rician mixture model classification algorithm for magnetic resonance images

A Rician mixture model classification algorithm for magnetic...

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IEEE International Symposium on Biomedical Imaging

作者： Snehashis Roy Aaron Carass Pierre-Louis Bazin Jerry L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Johns Hopkins University USA MedIC Neuroradiology Division Radiology and Radiological Science Johns Hopkins University USA

ISBN: (纸本)9781424439317

Tissue classification algorithms developed for magnetic resonance images commonly assume a Gaussian model on the statistics of noise in the image. While this is approximately true for voxels having large intensities, it is less true as the underlying intensity becomes smaller. In this paper, the Gaussian model is replaced with a Rician model, which is a better approximation to the observed signal. A new classification algorithm based on a finite mixture model of Rician signals is presented wherein the expectation maximization algorithm is used to find the joint maximum likelihood estimates of the unknown mixture parameters. Improved accuracy of tissue classification is demonstrated on several sample data sets. It is also shown that classification repeatability for the same subject under different MR acquisitions is improved using the new method.

关键词： Rician channels Classification algorithms Magnetic resonance image segmentation Histograms Biomedical imaging Maximum likelihood estimation image analysis Laboratories Radiology

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