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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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Iranian Conference of Biomedical engineering (ICBME)

作者： AmirEhsan Lashkari Hamid Soltanian-Zadeh GholamAli Hosseinzadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA

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.

关键词： Correlation Magnetic resonance imaging Independent component analysis Educational institutions Biomedical engineering Probabilistic logic Basal ganglia

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Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function

Improved particle swarm optimization and applications to Hid...

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IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA)

作者： Saeed Motiian 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 Hospital Detroit MI USA

Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.

关键词： Hidden Markov models Training Particle swarm optimization Speech processing Educational institutions Algorithm design and analysis Optimization

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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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Iranian Machine Vision and image processing (MVIP)

作者： Shaghayegh Eshaghian Gholam-Ali Hossein-Zadeh 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 Hospital Detroit MI USA

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 inter- subject 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.

关键词： Feature extraction Accuracy Drugs Brain Support vector machine classification Visualization Humans

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Traffic speed measurement using Spatio-Temporal model and frequency domain analysis

Traffic speed measurement using Spatio-Temporal model and fr...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Arucha Rungchokanun Vutipong Areekul Supakorn Siddhichai Hiroaki Kunieda Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Thailand Image Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Communication and Integrated Systems Tokyo Institute of Technology Meguro Tokyo Japan

In traffic monitoring applications, traffic speed is an important parameter of traffic management. The method for traffic speed measurement using video based on Spatio-Temporal (ST) model and frequency domain analysis is proposed in this paper. This method is designed to be able to measure the traffic speed in every pattern of road. The novel of proposed method is unfolding the edge information on ST model and analyse them in frequency domain to determine traffic speed. The proposed method is evaluated by compare with actual speed. The traffic speed is measured accurately 97.1%.

关键词： Analytical models Sensors Pixel image sensors Monitoring

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Sport Video Classification Using an Ensemble Classifier

Sport Video Classification Using an Ensemble Classifier

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Iranian Machine Vision and image processing (MVIP)

作者： Mohamad Hoseyn Sigari Samaneh Abbasi Sureshjani Hamid Soltanian-Zadeh Control & Intelligent Processing Center of Excellence (CIPCE) School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA

Sport video classification is an application of video analysis which can be useful in video indexing and retrieval. In this article, a new method for sport video classification using ensemble classifier is proposed. The proposed method uses 6 features: 3 dominant colors, dominant gray level, cut rate and motion rate. These features are classified by 4 simple classifiers in an ensemble classifier: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), Decision Tree (DT) and Probabilistic Neural Network (PNN). To combine the output of simple classifiers and make final decision, weighted majority vote is used while the weight of each simple classifier is equal to corresponding correct classification rate (CCR). Experimental result shows that the CCR of proposed system is 78.8%. In this experiment, 104 clips in 7 different sport classes are used: football, basketball, tennis, swimming, futsal, ski and box.

关键词： Feature extraction Color image color analysis Classification algorithms Accuracy Artificial neural networks Histograms

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Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

Universal Steganalysis Based on Local Prediction Error in Wa...

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International Conference on Intelligent Information Hiding and Multimedia Signal processing (IIHMSP)

作者： Anahita Shojaei-Hashemi Mostafa Mehdipour-Ghazi Shahrokh Ghaemmaghami Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.

关键词： Equations Mathematical model Payloads Transform coding Correlation Educational institutions Reliability

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Effective tree scattering at L-band

Effective tree scattering at L-band

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Mehmet Kurum Peggy E. O'Neill Roger H. Lang Alicia T. Joseph Michael H. Cosh Thomas J. Jackson Hydrological Sciences Branch / Code 614.3 NASA Goddard Space Flight Center Greenbelt MD USA Department of Electrical & Computer Engineering George Washington University Washington DC USA Hydrology & Remote Sensing Laboratory USDA ARS Beltsville MD USA

This paper investigates tree scattering effects at L-band by using a first-order radiative transfer (RT) model and truck-based measurements of brightness temperature over natural conifer stands to assess the performance of the τ - ω (tau-omega) model, a zero-order RT solution, over forest canopies. The tau-omega model accounts for vegetation effects in terms of "effective" vegetation parameters (single-scattering albedo and vegetation opacity) which represent the canopy as a whole. This approach inherently ignores multiple-scattering effects and it thus has a limited validity depending on the level of scattering within the canopy. The fact that the scattering from large forest components such as branches and trunks is significant at L-band requires that retrieved vegetation parameters be evaluated (compared) with their theoretical definitions to provide better understanding of these parameters in the soil moisture (SM) retrievals over moderately to densely vegetated landscapes. In this paper, the tau-omega model is fitted to a first-order RT model with an "effective" albedo assuming that "effective" vegetation optical depth is same as the "theoretical" opacity [1]. The "effective" albedo is found to be less than half of the "theoretical" one, which is generally around 0.5-0.6 for tree canopies at L-band. The "effective" albedo differs from the albedo of a single forest canopy element and becomes a global parameter which depends on all the processes taking place within the canopy including multiple-scattering and ground reflection.

关键词： Scattering Vegetation Vegetation mapping Microwave radiometry Microwave measurements L-band

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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Middle East Conference on Biomedical engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Diseases image segmentation Biomedical imaging Gaussian distribution

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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Middle East Conference on Biomedical engineering (MECBME)

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours image segmentation Charge carrier processes

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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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Iranian Conference of Biomedical engineering (ICBME)

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

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.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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