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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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Appropriate twinkling frequency and inter-sources distance selection in SSVEP-based HCI systems

Appropriate twinkling frequency and inter-sources distance s...

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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： Seyed Navid Resalat Seyed Kamaledin Setarehdan 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 Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering College of Engineering Sharif University of Technology Tehran Iran

Steady-State Visual Evoked Potentials (SSVEPs) are one of the most important EEG signals used in Human computer Interface (HCI) systems. These signals are generated by Looking at flickering external light sources stimulating the central part of the retina. By increasing the number of external light sources, detection of the corresponding SSVEPs from the recorded EEG signal becomes more complicated. On the other hand, the ratio of the sensitivity to specificity in high-speed classifiers becomes more significant. This study presents the effect of the twinkling frequencies and the inter-sources distance of two Light Emitting Diodes (LEDs) on the ratio of the sensitivity to specificity of the two classes. The features used for signal classification is the amplitude of the main frequencies in the spectrum of each frequency pairs that is simply classified by the Max classifier. The purpose of this is to find the best twinkling frequencies and the best inter-sources distance among a set of predefined values when there are only two light sources in order to nearly equalize the sensitivity and the specificity. For this aim, seven different frequency pairs of LEDs in five distinct inter-sources distances are examined and it is shown that the best frequency pair is 10 and 15 Hz with inter-sources distances of 24 or 44 cm.

关键词： Light sources Sensitivity Light emitting diodes Electroencephalography Educational institutions Steady-state Visualization

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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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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

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

ISBN: (纸本)9781424470006

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). © 2011 IEEE.

关键词： computerized tomography

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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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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

ISBN: (纸本)9781424470006

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. © 2011 IEEE.

关键词： Pulmonary diseases

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Effect of Different Diffusion Maps on Registration Results

Effect of Different Diffusion Maps on Registration Results

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Iranian Machine Vision and image processing (MVIP)

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 Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and EAR indices are superior to those obtained using only one of them alone.

关键词： Ear Diffusion tensor imaging Tensile stress Registers Eigenvalues and eigenfunctions Anisotropic magnetoresistance image registration

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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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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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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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