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检索条件"机构=Center of Excellence in Signal and Image Processing"

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A Novel Local Spline Smoothing-Based Approach for Medical Ultrasound images Enhancement

A Novel Local Spline Smoothing-Based Approach for Medical Ul...

引用

IEEE-EMBS Conference on Biomedical Engineering and Sciences

作者： Fardin Afdideh Roya Khasheei Rad Seyed Navid Resalat Elyas Sabeti IEEE Member Biomedical Engineering Department Islamic Azad University Control and Intelligent Processing Center of Excellence University of Tehran Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology

ISBN: (纸本)9781467316644

In this work, a novel local spline smoothing-based approach is proposed in order to despeckling medical ultrasound images. For better comparison, firstly nine conventional statistical spatial filters were applied on the ultrasound images and the results evaluated by quantitative indices of contrast to noise ratio (CNR) and speckle signal to noise ratio (SSNR). Secondly, one-dimensional cubic spline smoothing (1DCSS) function was applied on the whole speckled image as the first proposed approach. Finally, as the second proposed approach, 1DCSS was applied locally on the sliding windows of the ultrasound image then conventional statistical spatial filters were applied. The results indicate that the proposed approaches outperform the existing conventional ones.

关键词： Cubic spline smoothing Despeckling Medical ultrasound image Spatial filters

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Interpolation of Orientation Distribution Functions (ODFs) in Q-ball Imaging

Interpolation of Orientation Distribution Functions (ODFs) i...

引用

Iranian Conference of Biomedical Engineering

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

ISBN: (纸本)9781467331289

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.

关键词： High angular resolution diffusion imaging (HARDI) Q-ball imaging Orientation distribution function (ODF) Interpolation Principal diffusion direction (PDD)

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High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances

High-speed SSVEP-based BCI: Study of various frequency pairs...

引用

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

作者： Seyed Navid Resalat Valiallah Saba 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 AJA University of Medical Sciences Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering College of Engineering Sharif University of Technology Tehran Iran

Brain Computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important. In this work, a SSVEP-based BCI with two external stimulants was designed. In order to determine the best twinkling frequency of stimulants and the best distance between them, the classification accuracy for seven different twinkling frequency pairs and five different stimulants distances was calculated. Two methods for feature extraction step were proposed and the Max classifier was used for classification in order to speed up the computational burden of classification step. Features were extracted from four different segment lengths (sweeps). The results showed that nearly in all sweeps and all inter-sources distances the frequency pair of 10-15 Hz has the highest classification accuracy among other frequency pairs, which is 92% in the inter-sources distance of 24 cm and sweep length of 3 seconds. In addition, the results demonstrated that the method 2 feature extraction technique outperforms the method 1. In addition, for determining the best sweep length, Information Transfer Rate (ITR) was computed and the results indicated that the sweep length of 0.5 second has the highest ITR, so would be practical in real-time applications of BCI.

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Development of a MATLAB-Based Toolbox for Brain Computer Interface Applications in Virtual Reality

Development of a MATLAB-Based Toolbox for Brain Computer Int...

引用

Iranian Conference on Electrical Engineering

作者： Fardin Afdideh Mohammad Bagher Shamsollahi Seyed Navid Resalat Biomedical Signal and Image Processing Laboratory (BiSPL) School of Electrical Engineering Sharif University of Technology Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran

ISBN: (纸本)9781467311496

Brain computer interface (BCI) is a widely used system to assist the disabled and paralyzed people by creating a new communication channel. Among the various methods used in BCI area, motor imagery (MI) is the most popular and the most common one due to its the most natural way of communication for the subject. Some software applications are used to implement BCI systems, and some toolboxes exist for EEG signal processing. In recent years virtual reality (VR) technology has entered into the BCI research area to simulate the real world situations and enhance the subject performance. In this work, a completely MATLAB-based Mi-based BCI system is proposed and implemented in order to navigate into a virtual environment. In addition, a variety of features types were employed to select the best ones in the proposed system with the use of linear discriminant analysis (LDA) classifier through some interactive graphical user interfaces (GUIs). The results show the feasibility of the proposed BCI system in the subject training with or without feedback and even navigation into a virtual home.

关键词： Brain Computer Interface (BCI) MATLAB Motor imagery (MI) Virtual Reality (VR) Real-time EEG signal processing

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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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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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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

引用

2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Brain

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Experimental protocol for accessibility evaluation

Experimental protocol for accessibility evaluation

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作者： Lima, Ana C.O. De Fátima Q. Vieira, Maria Aguiar, Yuska P.C. LIHM Electric Engineering Department Federal University of Campina Grande - UFCG Campina Grande Paraiba Brazil Center for Excellence in Signal and Image Processing Dept. of Electronic and Electrical Engineering University of Strathclyde United Kingdom

ISBN: (纸本)9789728939182

This paper presents an experimental protocol for the assessment of devices and systems' accessibility to be used by people with visual impairments. This protocol was adapted from an experimental protocol originally conceived for usability evaluation of products and systems at the Laboratory of Human-Machine Interfaces (LIHM) at the Federal University of Campina Grande (UFCG), in Brazil. The adoption of a systematic approach when performing the accessibility evaluation gives the evaluation results a more reliable basis for claims as well as enabling to replicate the experiments performed. The paper presents the protocol and discusses how to employ it in the evaluation of a set of resources conceived to promote accessibility for people with visual impairments.

关键词： Ergonomics

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Programming a user model with data gathered from a user profile

Programming a user model with data gathered from a user prof...

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作者： Scherer, Daniel Netto, Ademar V. De Fátima Q. Vieira, Maria Aguiar, Yuska P.C. LIHM Electric Engineering Department Federal University of Campina Grande UFCG Campina Grande Paraiba Brazil LIHM Electric Engineering Department State University of Paraiba Campina Grande Paraiba Brazil Center for Excellence in Signal and Image Processing Dept. of Electronic and Electrical Engineering University of Strathclyde United Kingdom

ISBN: (纸本)9789728939182

User profiles have been the basis for the user in terface conception process, nonetheless these con sist mainly of a concise set of user characteristics that do not account for the dynamics of the interaction with the systems, along which errors might occur. On the other hand, user programmable models have often been used to support system adaptability features and automatic testing. This work is situated in the context of human error prevention through the design of ergonomic user interfaces for automated industrial systems, and more specifically for electric systems. This paper presents the relationship between a User Profile (POCUs) and a programmable model (conceived to represent the user behaviour of operators during task completion in an electric system substation). The paper also illustrates how to instantiate the programmable model parameters based on data from the User Profile.

关键词： Errors

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

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