Dr. Hossein Rabbani Profile

Dr. Hossein Rabbani

Associate Professor at Isfahan Univ of Medical Sciences

Area of Expertise:

medical image analysis and modeling , statistical (multidimensional) signal processing , sparse transforms , image restoration , ophthalmic image analysis , biomedical signal processing

Websites:

Personal Website

Profile Summary

I received the B.Sc. degree in Electrical Engineering (Communications) from Isfahan University of Technology, Isfahan, Iran, in 2000 with the highest honors, and the M.Sc. and Ph.D. degrees in Bioelectrical Engineering in 2002 and 2008, respectively, from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. In 2007 I was with the Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada, as a Visiting Researcher, and in 2011 with the University of Iowa, IA, United States, as a Postdoctoral Research Scholar. I am now an Associate Professor in Biomedical Engineering Department and Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. I am also currently with Duke University Eye Center from 2013.
I am a Senior Member of IEEE (Signal Processing Society, Engineering in Medicine and Biology Society, Circuits and Systems Society, Computer Society), and Editor-in-Chief of Journal of Medical Signals and Sensors. My main research interests are medical image analysis and modeling, statistical (multidimensional) signal processing, sparse transforms, and image restoration, which more than 110 papers and book chapters have been published by me as an author or co-author in these areas.

Publications (16)

SPIE Journal Paper | 24 March 2018

Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani

JBO Vol. 23 Issue 03

KEYWORDS: Optical coherence tomography, Image restoration, Denoising, Associative arrays, Reconstruction algorithms, Lawrencium, Visualization, Signal to noise ratio, Image processing, Image quality

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SPIE Journal Paper | 21 March 2018

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

Reza Rasti, Alireza Mehridehnavi, Hossein Rabbani, Fedra Hajizadeh

JBO Vol. 23 Issue 03

KEYWORDS: Optical coherence tomography, Feature extraction, Macula, Data modeling, Convolutional neural networks, Diagnostics, Fermium, Databases, Performance modeling, 3D image processing

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SPIE Journal Paper | 29 November 2017

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels

Leyuan Fang, Chong Wang, Shutao Li, Jun Yan, Xiangdong Chen, Hossein Rabbani

JBO Vol. 22 Issue 11

KEYWORDS: Optical coherence tomography, Principal component analysis, Composites, 3D image processing, Image classification, Feature extraction, 3D modeling, Network security, Convolution, Coherence (optics)

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SPIE Journal Paper | 29 August 2017

Optical coherence tomography image denoising using Gaussianization transform

Zahra Amini, Hossein Rabbani

JBO Vol. 22 Issue 08

KEYWORDS: Optical coherence tomography, Denoising, Image denoising, Image segmentation, Filtering (signal processing), Data modeling, Image processing, Speckle, Visualization, Associative arrays

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SPIE Journal Paper | 23 June 2017

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

Leyuan Fang, Liumao Yang, Shutao Li, Hossein Rabbani, Zhimin Liu, Qinghua Peng, Xiangdong Chen

JBO Vol. 22 Issue 06

KEYWORDS: Optical coherence tomography, Sensors, Eye

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Proceedings Article | 9 December 2015 Paper

Paper

Automatic segmentation of Leishmania parasite in microscopic images using a modified CV level set method

Maria Farahi, Hossein Rabbani, Ardeshir Talebi, Omid Sarrafzadeh, Shahab Ensafi

Proc. SPIE. 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015)

KEYWORDS: Image processing algorithms and systems, Liver, Image segmentation, Image processing, Error analysis, Image resolution, Bone, Medical imaging, Statistical modeling, Binary data

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Proceedings Article | 20 March 2014 Paper

Paper

Selection of the best features for leukocytes classification in blood smear microscopic images

Omid Sarrafzadeh, Hossein Rabbani, Ardeshir Talebi, Hossein Usefi Banaem

Proc. SPIE. 9041, Medical Imaging 2014: Digital Pathology

KEYWORDS: Blood, Image segmentation, Pathology, Diagnostics, Feature extraction, Image filtering, Kidney, Image classification, Fuzzy logic, RGB color model

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Proceedings Article | 13 March 2014 Paper

Paper

Comparison of macular OCTs in right and left eyes of normal people

Tahereh Mahmudi, Rahele Kafieh, Hossein Rabbani, Alireza Mehri dehnavi, Mohammadreza Akhlagi

Proc. SPIE. 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Eye, Nerve, Visualization, Optical coherence tomography, Retina, Diffusion, Medical imaging, Tolerancing, Macula, 3D image processing

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Proceedings Article | 13 March 2014 Paper

Paper

A statistical model for 3D segmentation of retinal choroid in optical coherence tomography images

F. Ghasemi, H. Rabbani

Proc. SPIE. 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Image processing algorithms and systems, Statistical analysis, Data modeling, Optical coherence tomography, Image segmentation, Retina, Error analysis, Eye models, Expectation maximization algorithms, 3D image processing

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Proceedings Article | 26 September 2013 Paper

Paper

Optical Coherence Tomography noise reduction over learned dictionaries with introduction of complex wavelet for start dictionary

Raheleh Kafieh, Hossein Rabbani

Proc. SPIE. 8858, Wavelets and Sparsity XV

KEYWORDS: Wavelet transforms, Speckle, Optical coherence tomography, Digital filtering, Wavelets, Denoising, Image filtering, Associative arrays, Nonlinear filtering, Continuous wavelet transforms

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Proceedings Article | 26 September 2013 Paper

Paper

Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

Shirin Hajeb Mohammad Alipour, Hossein Rabbani

Proc. SPIE. 8856, Applications of Digital Image Processing XXXVI

KEYWORDS: Visualization, Surgery, Blood, Blood vessels, Image segmentation, Retina, Pathology, Angiography, Image classification, Macula

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Proceedings Article | 18 January 2010 Paper

Paper

A fast method for video deblurring based on a combination of gradient methods and denoising algorithms in Matlab and C environments

Zeynab Mirzadeh, Razieh Mehri, Hossein Rabbani

Proc. SPIE. 7529, Image Quality and System Performance VII

KEYWORDS: Point spread functions, MATLAB, Image processing, Video, Denoising, Computer programming, Computer simulations, Deconvolution, C++, Expectation maximization algorithms

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In this paper the degraded video with blur and noise is enhanced by using an algorithm based on an iterative procedure. In this algorithm at first we estimate the clean data and blur function using Newton optimization method and then the estimation procedure is improved using appropriate denoising methods. These noise reduction techniques are based on local statistics of clean data and blur function. For estimated blur function we use LPA-ICI (local polynomial approximation - intersection of confidence intervals) method that use an anisotropic window around each point and obtain the enhanced data employing Wiener filter in this local window. Similarly, to improvement the quality of estimated clean video, at first we transform the data to wavelet transform domain and then improve our estimation using maximum a posterior (MAP) estimator and local Laplace prior. This procedure (initial estimation and improvement of estimation by denoising) is iterated and finally the clean video is obtained. The implementation of this algorithm is slow in MATLAB1 environment and so it is not suitable for online applications. However, MATLAB has the capability of running functions written in C. The files which hold the source for these functions are called MEX-Files. The MEX functions allow system-specific APIs to be called to extend MATLAB's abilities. So, in this paper to speed up our algorithm, the written code in MATLAB is sectioned and the elapsed time for each section is measured and slow sections (that use 60% of complete running time) are selected. Then these slow sections are translated to C++ and linked to MATLAB. In fact, the high loads of information in images and processed data in the "for" loops of relevant code, makes MATLAB an unsuitable candidate for writing such programs. The written code for our video deblurring algorithm in MATLAB contains eight "for" loops. These eighth "for" utilize 60% of the total execution time of the entire program and so the runtime should be potentially decreased by considering these loops in C. However, after implementing eighth "for" in C using the MEX library, the measured run time unexpectedly increased. According to the timing in MEX, this runtime increscent is not because of connecting MATLAB to MEX but it is related to loops. Our simulation shows implementation of a loop in C++ takes two times more than the same loop in MATLAB. In spite of C functions, the OpenCV2 functions take less time, so OpenCV that is an open source computer vision library written in C and C++ (and it is an active development on interfaces for MATLAB) would be useful for getting more speed. After implementation the "for" loops of our algorithm using OpenCV library, our simulations show the run time decreases a lot.

Proceedings Article | 24 September 2007 Paper

Paper

Local bivariate Cauchy distribution for video denoising in 3D complex wavelet domain

Hossein Rabbani, Mansur Vafadust, Ivan Selesnick

Proc. SPIE. 6696, Applications of Digital Image Processing XXX

KEYWORDS: Signal to noise ratio, Wavelet transforms, Video, Wavelets, Denoising, Discrete wavelet transforms, 3D modeling, Transform theory, Video processing, 3D image processing

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Proceedings Article | 20 September 2007 Paper

Paper

Modeling statistical properties of wavelets using a mixture of bivariate cauchy models and its application for image denoising in complex wavelet domain

Hossein Rabbani, Mansur Vafadust, Ivan Selesnick

Proc. SPIE. 6701, Wavelets XII

KEYWORDS: Signal to noise ratio, Wavelet transforms, Visualization, Image processing, Wavelets, Denoising, Image denoising, Statistical modeling, Performance modeling, Expectation maximization algorithms

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Proceedings Article | 20 September 2007 Paper

Paper

Wavelet-based denoising using local Laplace prior

Hossein Rabbani, Mansur Vafadust, Ivan Selesnick

Proc. SPIE. 6701, Wavelets XII

KEYWORDS: Wavelet transforms, Visualization, Video, Wavelets, Denoising, Error analysis, Discrete wavelet transforms, Transform theory, Video processing, 3D image processing

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Proceedings Article | 25 April 2007 Paper

Paper

Image denoising in complex wavelet domain using a mixture of bivariate laplacian distributions with local parameters

Hossein Rabbani, Mansur Vafadust

Proc. SPIE. 6575, Visual Information Processing XVI

KEYWORDS: Wavelet transforms, Visual process modeling, Visualization, Image processing, Wavelets, Denoising, Discrete wavelet transforms, Image denoising, Systems modeling, Expectation maximization algorithms

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Showing 5 of 16 publications

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