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    Brain tumor segmentation based on 3D neighborhood features using rule-based learning

    , Article 11th International Conference on Machine Vision, ICMV 2018, 1 November 2018 through 3 November 2018 ; Volume 11041 , 2019 ; 0277786X (ISSN); 9781510627482 (ISBN) Barzegar, Z ; Jamzad, M ; Sharif University of Technology
    SPIE  2019
    Abstract
    In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

    Brain tumor segmentation based on 3D neighborhood features using rule-based learning

    , Article 11th International Conference on Machine Vision, ICMV 2018, 1 November 2018 through 3 November 2018 ; Volume 11041 , 2019 ; 0277786X (ISSN) ; 9781510627482 (ISBN) Barzegar, Z ; Jamzad, M ; Sharif University of Technology
    SPIE  2019
    Abstract
    In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

    An Investigation of Resting-State Eeg Biomarkers Derived from Graph of Brain Connectivity for Diagnosis of Depressive Disorder

    , M.Sc. Thesis Sharif University of Technology Arabpour, Mohammad Reza (Author) ; Hajipour, Sepideh (Supervisor)
    Abstract
    Among the most costly diseases that affect a person's quality of life throughout his or her life, mental disorders (excluding sleep disorders) affect up to 25 percent of people in any community. One of the most common types of these disorders in Iran is depressive disorder, which according to official statistics, 13% of Iranians have some symptoms of it. Until now, the diagnosis of this disease has been traditionally done in clinics with interviews and questionnaires tests based on behavioral psychology and using symptom assessment. Therefore, there is a relatively low accuracy in the treatment process. Nowadays, with the help of functional brain imaging such as electroencephalogram (EEG)... 

    Speed control of a digital servo system using brain emotional learning based intelligent controller

    , Article PEDSTC 2013 - 4th Annual International Power Electronics, Drive Systems and Technologies Conference ; 2013 , Pages 311-314 ; 9781467344845 (ISBN) Jafari, M ; Shahri, A. M ; Shuraki, S. B ; Sharif University of Technology
    2013
    Abstract
    In this paper, a biologically motivated controller based on a mammalian limbic system called Brain Emotional Learning Based Intelligent Controller (BELBIC) is used for speed control of a Digital Servo System. The proposed controller is applied experimentally to a laboratory Digital Servo System 1 via MATLAB external mode. Comparing results of the proposed controller with conventional PID controller shows satisfactory performance including faster response and lower overshoot  

    Traumatic brain injury caused by +Gz acceleration

    , Article ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 21 August 2016 through 24 August 2016 ; Volume 3 , 2016 ; 9780791850138 (ISBN) Shafiee, A ; Ahmadian, M.T ; Hoviattalab, M ; Computers and Information in Engineering Division; Design Engineering Division ; Sharif University of Technolgy
    American Society of Mechanical Engineers (ASME) 
    Abstract
    Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. This phenomenon has been under study for many years and yet it remains a question due to physiological, geometrical and computational complexity. Although the modeling facilities for soft tissue have improved, the precise CT-imaging of human head has revealed novel details of the brain, skull and meninges. In this study a 3D human head including the brain, skull, and meninges is modeled using CT-scan and MRI data of a 30-year old human. This model is named "Sharif University of Technology Head Trauma Model (SUTHTM)". By validating SUTHTM, the model is then used to study the... 

    Toward epileptic brain region detection based on magnetic nanoparticle patterning

    , Article Sensors (Switzerland) ; Volume 15, Issue 9 , September , 2015 , Pages 24409-24427 ; 14248220 (ISSN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Sharif University of Technology
    MDPI AG  2015
    Abstract
    Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and... 

    Brain activity modeling in general anesthesia: Enhancing local mean-field models using a slow adaptive firing rate

    , Article Physical Review E - Statistical, Nonlinear, and Soft Matter Physics ; Volume 76, Issue 4 , 2007 ; 15393755 (ISSN) Molaee Ardekani, B ; Senhadji, L ; Shamsollahi, M. B ; Vosoughi Vahdat, B ; Wodey, E ; Sharif University of Technology
    American Physical Society  2007
    Abstract
    In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross and Bojak and Liley mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the... 

    Optimum recovery time for cyclic compression tests on bovine brain tissue

    , Article Scientia Iranica ; Volume 26, Issue 4A , 2019 , Pages 2203-2211 ; 10263098 (ISSN) Mohajery, M ; Ahmadian, M. T ; Sharif University of Technology
    Sharif University of Technology  2019
    Abstract
    In conducting mechanical tests on the brain tissue, it is preferred to perform multiple tests on the same sample. In this study, we investigated the behavior of the bovine brain tissue in repeated compression tests wit h 0 recovery periods (namely, 10, 60, 120, 180, 240, and 300 s). Compression tests were performed on cylindrical samples with average diameter and height of 18.0 mm and 15.0 mm, respectively. Two testing protocols were employed; t he first one comprised experiments wit h 5, 25, and 125 mm/min loading speeds up to 33% strain and the second one consisted of tests with 25 and 125 mm/min loading speeds up to 17% strain. Each experiment was conducted in two cycles separated by a... 

    Optimum recovery time for cyclic compression tests on bovine brain tissue

    , Article Scientia Iranica ; Volume 26, Issue 4A , 2019 , Pages 2203-2211 ; 10263098 (ISSN) Mohajery, M ; Ahmadian, M. T ; Sharif University of Technology
    Sharif University of Technology  2019
    Abstract
    In conducting mechanical tests on the brain tissue, it is preferred to perform multiple tests on the same sample. In this study, we investigated the behavior of the bovine brain tissue in repeated compression tests wit h 0 recovery periods (namely, 10, 60, 120, 180, 240, and 300 s). Compression tests were performed on cylindrical samples with average diameter and height of 18.0 mm and 15.0 mm, respectively. Two testing protocols were employed; t he first one comprised experiments wit h 5, 25, and 125 mm/min loading speeds up to 33% strain and the second one consisted of tests with 25 and 125 mm/min loading speeds up to 17% strain. Each experiment was conducted in two cycles separated by a... 

    Source Localization of EEG in Early Alzheimer’s Disease

    , M.Sc. Thesis Sharif University of Technology Salami, Mohsen (Author) ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
    Abstract
    Localization of electrical activity in the brain is one of the major problems in cognitive science and neuroscience. Indeed, Source localization is the inverse processing procedure on brain signals to estimate the location and position of resources in the human brain. Current technics for neurological imaging is included fMRI، PET، MEG and ERP. These methods is not appropriated to answer the question that when does each of different components of the brain begin their activity. The EEG signals could be useful to eliminate some of limitations of above methods. The problem with EEG signals collected from the skull is that they don’t refer directly to the location of active neurons. The... 

    Modelling and analysis of the effect of angular velocity and acceleration on brain strain field in traumatic brain injury

    , Article ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) ; Volume 3 A , 2013 ; 9780791856215 (ISBN) Hoursan, H ; Ahmadian, M. T ; Barari, A ; Beidokhti, H. N ; Sharif University of Technology
    Abstract
    Traumatic brain injury (TBI) has long been known as one of the most anonymous reasons for death around the world. A presentation of a model of what happens in the process has been under study for many years; and yet it remains a question due to physiological, geometrical and computational complications. Although the facilities for soft tissue modeling have improved and the precise CT-imaging of human head has revealed novel details of brain, skull and the interface (the meninges), a comprehensive FEM model of TBI is still being studied. This study aims to present an optimized model of human head including the brain, skull, and the meninges after a comprehensive study of the previous models.... 

    EEG-based functional brain networks: does the network size matter?

    , Article PloS one ; Volume 7, Issue 4 , 2012 ; 19326203 (ISSN) Joudaki, A ; Salehi, N ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    PLOS  2012
    Abstract
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of... 

    Effect of mozart music on hippocampal content of BDNF in postnatal rats

    , Article Basic and Clinical Neuroscience ; Volume 2, Issue 3 , 2011 , Pages 21-26 ; 2008126X (ISSN) Marzban, M ; Shahbazi, A ; Tondar, M ; Soleimani, M ; Bakhshayesh, M ; Moshkforoush, A ; Sadati, M ; Zendehrood, S. A ; Joghataei, M. T ; Sharif University of Technology
    2011
    Abstract
    Introduction: It has shown that listening to Mozart music can potentiate spatial tasks in human; and reduce seizure attacks in epileptic patients. A few studies have reported the effects of prenatal plus postpartum exposure of mice to the Mozart music on brain-drived neurotrophic factor (BDNF) in the hippocampus. Here we investigated the effect of postpartum exposure to The Mozart music on BDNF concentration in the hippocampus of rat. Methods: Thirty male one day old newborn Wistar rats divided randomly in two equal experimental and control groups. Experimental group exposed to slow rhythm Mozart music (Mozart Sonata for two pianos KV 448, 6 hour per day; sound pressure levels, between 80... 

    Resiliency of cortical neural networks against cascaded failures

    , Article NeuroReport ; Volume 26, Issue 12 , 2015 , Pages 718-722 ; 09594965 (ISSN) Jalili, M ; Sharif University of Technology
    Lippincott Williams and Wilkins  2015
    Abstract
    Network tools have been extensively applied to study the properties of brain functional and anatomical networks. In this paper, resiliency of Caenorhabditis elegans cortical networks against cascaded failures is studied. To this end, directed network formed by chemical connections and undirected network formed by electrical couplings through gap junctions are considered. Furthermore, two types of C. elegans networks are studied: the whole cortical network of the hermaphrodite type and the network of the posterior cortex in male C. elegans. The results show that resiliency of hermaphrodite and male networks is different. The male cortical network of chemical synapses shows extensively weaker... 

    Superparamagnetic nanoparticles for epilepsy detection

    , Article World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015 ; Volume 51 , June , 2015 , Pages 1237-1240 ; 16800737 (ISSN) ; 9783319193878 (ISBN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Jaffray D. A ; Sharif University of Technology
    Springer Verlag  2015
    Abstract
    Epilepsy is the most common neurological disorder that is known with uncontrolled seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. The precise detection of an epileptic zone is key to its treatment. In this paper, we propose a method of epilepsy detection using brain magnetic field. The possibility of superparamagnetic nanoparticle (SPMN) as sensors for the detection of the epileptic area inside the brain is investigated. The aggregation of nanoparticles in the weak magnetic field of epileptic brain is modeled using potential... 

    Functional brain networks in parkinson's disease

    , Article 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, 30 November 2017 through 1 December 2017 ; 2018 ; 9781538636091 (ISBN) Akbari, S ; Fatemizadeh, E ; Reza Deevband, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    Parkinson's disease (PD) is the second most common and progressive neurological disorder. Parkinson's signs are caused by dysfunction in PD patient's brain network. Newly, resting state functional magnetic resonance imaging has been utilized to assess the altered functional connectivity in PD patients. In this study, we investigated the properties of the brain network topology in 19 PD patients compared to 17 normal healthy group by means of graph theory. In addition, we used four different graph formation methods to explore linear and nonlinear relationships between fMRI signals. Each correlation measure created a weighted graph for each subject. Different graph characteristics have been... 

    Estimation of effective brain connectivity with dual kalman filter and EEG source localization methods

    , Article Australasian Physical and Engineering Sciences in Medicine ; Volume 40, Issue 3 , 2017 , Pages 675-686 ; 01589938 (ISSN) Rajabioun, M ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Sharif University of Technology
    Abstract
    Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective... 

    EEG Brain Functional Network Analysis in Cortex Level

    , M.Sc. Thesis Sharif University of Technology Pedrood, Bahman (Author) ; Jalili, Mahdi (Supervisor)
    Abstract
    Complex networks science have received tremendous attention in recent years and the brain is one of the systems to which graph theoretical tools have been applied. Alzheimer’s disease (AD) is a neurodegenerative disease affecting many of elderly population. AD changes the anatomy of the brain, which subsequently results in changes in its functions. These changes have been frequently reported in signals recorded from the brain (such as MEG, fMRI and EEG). Among these neuroimaging techniques EEG is one of the most aproprate methods for extracting functional connectivites according to high temporal resolution. In this thesis, we aimed at analyzing the properties of EEG-based functional networks... 

    Classification of EEG Signals to Detect Predefined Words in Imagined Speech

    , M.Sc. Thesis Sharif University of Technology Rajabli, Reza (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Attention to the Brain Computer interfaces (BCI) because of their potentials in improving, enhancing and substituting daily task, especially in people who suffer from diseases, has been increasing in the recent years. Such systems, receive brain activities and by extracting suitable features, try to interpret the brain commands. The aim of this project is to explore the ability of electroencephalogram (EEG) signal for silent communication by means of decoding imagined speech in brain activities. The previous research results show that imagining a word in the mind causes changes in the brain signals. These changes are interchangeable among different words. As a result, discriminating between... 

    Modeling of Visual Attention Mechanism by Brain Signals

    , M.Sc. Thesis Sharif University of Technology Pahlevan Aghababa, Fatemeh (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Attention is a cognitive process in which the mind reacts to certain stimuli or stimuli of the environment while other environmental stimuli are ignored. Attention might be an overt or covert process. Overt attention is a process in which based on the purpose, we selectively choose an object or place among other objects and places to focus on and we are aware of it. However, the covert attention originates from hidden source, and we are not aware of it. In fact, the covert attention causes a clear and rapid movement of the eye toward the stimulus or space to be taken into consideration and the time when the movement of the eye it means overt attention has occurred. Visual attention is given...