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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) 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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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)

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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State Estimation for a Zero-Dimensional Electrochemical Model of Lithium-Sulfur Batteries

arXiv

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arXiv 2021年

作者： Huang, Zhijia Zhang, Dong Couto, Luis D. Yang, Quan-Hong Moura, Scott J. Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Civil and Environmental Engineering University of California BerkeleyCA94720 United States Department of Control Engineering and System Analysis Université Libre de Bruxelles BrusselsB-1050 Belgium State Key Laboratory of Chemical Engineering School of Chemical Engineering and Technology Tianjin University Tianjin300072 China

Lithium-sulfur (Li-S) batteries have become one of the most attractive alternatives over conventional Li-ion batteries due to their high theoretical specific energy density (2500 Wh/kg for Li-S vs. ∼250 Wh/kg for Li-ion). Accurate state estimation in Li-S batteries is urgently needed for safe and efficient operation. To the best of the authors’ knowledge, electrochemical model-based observers have not been reported for Li-S batteries, primarily due to the complex dynamics that make state observer design a challenging problem. In this work, we demonstrate a state estimation scheme based on a zero-dimensional electrochemical model for Li-S batteries. The nonlinear differential-algebraic equation (DAE) model is incorporated into an extend Kalman filter. This observer design estimates both differential and algebraic states that represent the dynamic behavior inside the cell, from voltage and current measurements only. The effectiveness of the proposed estimation algorithm is illustrated by numerical simulation results. Our study unlocks how an electrochemical model can be utilized for practical state estimation of Li-S batteries. Copyright © 2021, The Authors. All rights reserved.

关键词： Lithium sulfur batteries

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State of charge estimation of parallel connected battery cells via descriptor system theory

arXiv

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arXiv 2020年

作者： Zhang, Dong Couto, Luis D. Benjamin, Sebastien Zeng, Wente Coutinho, Daniel F. Moura, Scott J. Department of Civil and Environmental Engineering University of California BerkeleyCA United States Department of Control Engineering and System Analysis Université Libre de Bruxelles BrusselsB-1050 Belgium Saft Batteries Total S.A. Total S.A. Department of Automation and Systems UFSC PO BOX 476 88040-900 Florianópolis SC Brazil

— This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO2/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear equations

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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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Consensus-Based Distributed Mean Square State Estimation

Consensus-Based Distributed Mean Square State Estimation

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American control Conference

作者： Carlos E. de Souza Michel Kinnaert Daniel Coutinho Department of Systems and Control Laboratorio Nacional de Computacao Cientifica - LNCC/MCTI Av. Getulio Vargas 333 Petropolis RJ 25651-075 Brazil Department of Control Engineering and System Analysis Universite Libre de Bruxelles (ULB) Brussels 1050 Belgium Department of Automation and Systems Universidade Federal de Santa Catarina PO Box 476 Florianopolis SC 88040-900 Brazil

ISBN: (纸本)9781479917730

This paper addresses the design of a distributed steady state filter for a sensor network. In the considered scenario, an estimate of the full system state is determined at each network node. The filter structure includes both measurement update terms from neighboring nodes, and a consensus term on the state estimates. The computation of the filter gains is recast as a convex optimization problem. Convergence of the estimation error variance is ensured at each network node and a guaranteed performance in the mean square sense is achieved. Extension of the method to linear parameter-varying systems yields a gain scheduled distributed filter.

关键词： network node mean square Filters linear parameter varying system Sensor networks Distribute State estimation Error measure measurement update

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

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

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.

关键词： Support vector machines Noise Magnetic resonance imaging Sensitivity Tumors Biomedical imaging Artificial neural networks

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms Image recognition Databases Training Humans

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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)

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 new scheme for evaluation of air-trapping in CT images

A new scheme for evaluation of air-trapping in CT images

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Iranian Machine Vision and Image Processing (MVIP)

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

Air trapping is an abnormal retention of air that occurs after expiration state in the lungs, observed in all types of bronchiolar and obstructive lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and bronchiolitis obliterans syndrome. Air trapping is often incidentally diagnosed on computed tomography (CT) scanning but this method needs doctors so it is subjective and depends on their experience. In this paper, we present a novel method for evaluation of air trapping in the lungs for detection of COPD in CT images. The proposed method finds volumetric variations of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared and the volumetric variations are used to classify the subjects. In the evaluated cases, the proposed method is able to estimate air trapping in the lungs from CT images without human intervention. This method may assist radiologists to measure and evaluate air trapping for detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Charge carrier processes Diseases Image segmentation Biomedical imaging

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