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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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A Multi-Robot System for Dome Inspection and Maintenance: Concept and Stability Analysis

A Multi-Robot System for Dome Inspection and Maintenance: Co...

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IEEE International Conference on Robotics and Biomimetics

作者： A. Nejadfard H. Moradi M. Nili Ahmadabadi School of Electrical and Computer Engineering the University of Tehran Control and Intelligent Processing Center of Excellence at the Electrical and Computer Engineering Department at the University of Tehran

ISBN: (纸本)9781457721366

In this paper a novel approach based on multi-robot cooperation is introduced for inspection and repair of dome structures. In this application, three robots which can be extended to more, connect to each other by three strings and cooperate such that all the robots are stable and at least one robot have access to all parts of the dome. The mathematical model of the system is derived and a systematic methodology is developed to find an optimal configuration, based on minimizing the strings' tensions or maximizing the stability, using Particle Swarm Optimization (PSO) algorithm and quadratic programming. The simulation results prove that there exists a stable path to fully sweep the surface of a dome. The experimental results on a small scale prototype validate these findings.

关键词： dome Stability analysis Dome Device Component Robot Robot System optimal configuration Cosmic strings Inspection stalls(animals)

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An investigation on the usage of image quality assessment in visual speech recognition

An investigation on the usage of image quality assessment in...

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2010 3rd International Congress on Image and Signal processing, CISP 2010

作者： Banitalebi, Amin Moosaei, Maryam Hossein-Zadeh, Gholam Ali Control and Intelligent Processing Center of Excellence College of Engineering University of Tehran Iran

ISBN: (纸本)9781424465149

Having a robust speech recognition scheme that can be relied upon in different environments is a strong requirement for modern systems. Previous works in field of lipreading mainly have used a level of segmentation at the beginning and then used the structure of the mouth, facial muscles of the speaker, some critical points on the lip, or the motion of these points for word recognition. In this paper we present a novel way of processing the video signal for lipreading application. We neither used segmentation level nor the extraction of important facial points. Instead, we've used HVS (human visual system) based image quality metrics, especially complex wavelet structural similarity (CW-SSIM) and visual information fidelity (VIF) as our similarity criterions. We used an intelligent frame by frame video comparison technique and we applied mentioned metrics in our approach. Experimental results showed that in comparison to other methods, this novel method can recognize the true letter among the letters of the utilized dictionary with an acceptable accuracy. ©2010 IEEE.

关键词： Image quality

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Seabed Tracking of an Autonomous Underwater Vehicle with Nonlinear Output Regulation

Seabed Tracking of an Autonomous Underwater Vehicle with Non...

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IEEE Conference on Decision and control

作者： A. Adhami-Mirhosseini A. Pedro Aguiar M. J. Yazdanpanah Control and Intelligent Processing Center of Excellence University of Tehran P.O. Box 14395/515 Tehran Iran Lab. of Robotics and Systems in Engineering and Science (LARSyS) Inst. Superior Tecnico (IST) Technical University of Lisbon Av. Rovisco Pais 1049-001 Lisboa Portugal

ISBN: (纸本)9781612848006

This paper proposes a new control algorithm for automatic seabed tracking performed by a low cost autonomous underwater vehicle subject to the fact that the seabed profile is not known in advance. The control design amounts to cast the problem in the framework of nonlinear output regulation theory combined with pseudo-spectral methods to approximate the solutions of the regulator equations. To this end, the seabed tracking problem is re-formulated as a trajectory tracking problem and the reference signal is constructed using Fourier series to approximate the seabed profiles. An interesting feature of this approach is that the combination of the Fourier series with output regulation problem allows to bypass the need to compute explicitly the Fourier coefficients. Stability analysis and simulation results with real seabed data show the effectiveness of the proposed controller.

关键词： Equations Regulators Mathematical model Vehicles Chebyshev approximation Vehicle dynamics

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Wavelet-based EEG denoising for automatic sleep stage classification

Wavelet-based EEG denoising for automatic sleep stage classi...

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International Conference on Electronics, Communications and Computers (CONIELECOMP)

作者： Edson Estrada Homer Nazeran Gustavo Sierra Farideh Ebrahimi S. Kamaledin Setarehdan Department of Electrical and Computer Engineering University of Texas El Paso El Paso TX USA Autónoma de Ciudad Juárez Instituto de Ciencias Biomédicas Universidad Chih Mexico Control and Intelligent Processing Center of excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In automatic sleep stage classification, as in any other signal processing task involving the easily contaminated EEG signals, denoising constitutes a crucial pre-processing step that must be addressed before carrying out further analysis on the EEG signals. Discrete wavelet transform offers an effective solution for denoising nonstationary signals such as EEG due to its shrinkage property. In this paper, we explored the application of wavelet denoising method to EEG signals acquired during different sleep stages classified according to the RK rules, with the objective to identify suitable thresholding rules and threshold values. Preliminary results showed that the combination of soft thresholding rule applied to the Detailed wavelet coefficients with the Universal threshold value produced better performance measures such as a smaller Minimum Squared Error (MSE) and a larger signal-to-Noise Ratio (SNR). Similarly improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4 and REM stage EEG signals using this combination. Such thresholding rule and values are equally well applicable to denoising EEG epochs acquired from deep sleep stages.

关键词： Electroencephalography Discrete wavelet transforms Noise reduction Signal to noise ratio Sleep Finite impulse response filter

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PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COGNITIVE RADIO SYSTEMS WITH INTERFERENCE CONSTRAINTS

PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COG...

引用

2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

作者： Saeed Motiian Mohammad Aghababaie Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Colleague of EngineeringUniversity of TehranTehran 14399-57131Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences(IPM)Tehran 19548-56316 Iran Image Analysis Laboratory Radiology DepartmentHenry Ford HospitalDetroitMichigan 48202USA

Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service（QoS）for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio（SINR） are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization（PSO）does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.

关键词： Cognitive radio Power allocation Particle Swarm Optimization（PSO）.

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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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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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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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Risk assessment of coronary arteries heart disease based on neuro-fuzzy classifiers

Risk assessment of coronary arteries heart disease based on ...

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17th Iranian Conference in Biomedical Engineering, ICBME 2010

作者： Zand, Mohammad Danesh Ansari, Amir Hossein Lucas, Caro Zoroofi, Reza Aghaee Zade Department of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

ISBN: (纸本)9781424474844

Coronary artery heart disease is one of the main reasons of death in under development countries such as Iran. Based on vagueness in data and uncertainty in decision making finding an optimal way for diagnosis would be helpful. The main goal of this article is to change the linguistic terms which doctors use for representing coronary heart disease possibility into classified stages. Indeed the medical diagnosis can be used as an issue of pattern recognition by an input vector of features introduced to a system so the corresponding risk of a particular disease can be estimated with this system. in this paper we used an adaptive network-based inference system in order to assess the risk of coronary artery heart diseases. With this point of view that fault in medical diagnosis are more errors of omission than of commission, we are going to reduce this diagnosis defect with Neuro-fuzzy networks such as Multi-Layer Perceptron (MLP) and an inference Neuro-fuzzy network such as ANFIS. In the last section we will compare these methods to show that ANFIS can obtain the best accommodation with doctor's opinions. © 2010 IEEE.

关键词： Diagnosis

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