Hossein Rabbani 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 he was with the Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada, as a Visiting Researcher, in 2011 with the University of Iowa, IA, United States, as a Postdoctoral Research Scholar, and in 2013-2014 with Duke University Eye Center as a Postdoctoral Fellow. He is now a Professor in Biomedical Engineering Department and Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Dr Rabbani is a Senior Member of IEEE, IEEE Signal Processing Society and IEEE Engineering in Medicine and Biology Society. His 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 him as an author or co-author in these areas.
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· Intra-retinal layer and fluid segmentation of 3D OCT images by deep learning
· Classification of ocular images using optimum basis functions (DL & MCA)
· OCT modeling: statistical modeling vs. geometrical modeling vs. energy-based modeling
· Synchronized analysis of EEG, MRI images, and SPECT images of patients suffering from seizure
· 3D Sparse Reconstruction of Cone-beam CT
· Multivariate Statistical Modeling of OCT Images
· Sparse Representation of PH Monitoring Signals
· Energy-based Modeling of OCT Images
· Sparse Representation of OCT Images
· Combination of graph based algorithms and time-frequency methods for processing of OCTs
· Seeking an appropriate feature extraction method for breast cancer recurrence prediction based on microarray gene expression data
· A new model based on Gaussianization of OCT data
· Automatic analysis of features of AMD in OCT images using 3D curvelet transform
· Automatic detection of acute myeloid leukemia in microscopic images using dictionary learning
· Automatic diagnosis of Mild Cognitive Impairment (MCI) by dictionary learning -based analysis of EEG signals
· 3D OCT Classification by Deep Learning
· Fully automated segmentation of fluid/cyst regions in OCT images using neutrosophic sets and graph algorithms
· Polyp detection/segmentation in video colonoscopy by convolutional neural network
· Image restoration using Gaussian mixture models with neighborhood nonlocal clustering
· Evaluation of the symmetricity of cup to disk ratio in left and right eyes of normal subjects
· Mosaicing macula OCT images and OCT optical disk
· Designing a dictionary for OCT images based on K-SVD algorithm using texture characteristics of retinal layers for image segmentation
· Automatic segmentation of corneal layer boundaries in OCT images and obtaining 3D maps of the entire thickness of cornea and inner layers
· Automatic detection of leishman bodies in bone marrow samples from patients with visceral leishmaniasis using level set method
· Evaluation of asymmetricity of right and left eyes of normal subjects using extracted features from optical coherence tomography (OCT) and color fundus images
· Automatic diagnosis of malaria based on complete circle-ellipse fitting search algorithm
· Automatic segmentation and recognition of lung nodules in thoracic CT images using active contour modeling and convex hull
· Segmentation of enhanced depth imaging optical coherence tomography (EDI-OCT) images using graph cut algorithm based on Gaussian mixture model of wavelet features
· Forming projection images from retinal layers on the 3D OCT data and fusion of them using curvelet transform to form an optimal projection image
· Evaluation of image pre-compensation methods for enhancing visual efficiency in the presence of higher order ocular optical aberrations
· Extraction of 15-lead ECG signal from vectorcardiogram (VCG) signal using partial linear transformation for providing information from posterior side of the heart
· Detection of foveal avascular zone (FAZ) based on curvelet transform for grading of diabetic retinopathy
· Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform
· A comparison between hp version of finite element method with EIDORS for electrical impedance tomography
· A comparison between ECG and VCG signals for detection of ischemia location
· Estimation of somatosensory evoked potentials with multiadaptive filters
· A contourlet-based watermarking method for medical images
· Automatic detection of diabetic retinopathy by extraction of retinal image features in curvelet domain
· Estimating depth of anesthesia based on wavelet transform and neuro-fuzzy systems
· Microcalcification detection in mammographic images using fractal model in wavelet domain
· Persian script character recognition using PCA
· A comparison between ECG and VCG for detection of ischemia
· Automatic detection and recognition of lung nodule in CT image based on active contour
· Complexity analysis of EEG signals for Mild Cognitive Impairment (MCI) diagnosis
· 3D segmentation of proximal enamel lesions in micro-ct images
· Automatic analysis of tracheal acoustic signals for apnea detection and introducing new clinical indices of depth of sedation
· Statistical modeling of 3D OCT data by mixture model
· Transform based ellipse detection in microscopic images using elliptical basis functions
· Automatic extraction and recognition of myeloma cell in microscopic bone marrow aspiration images
· Extraction of candida fungus from pap smear images based on ridgelet transform for vulvovaginal candidiasis diagnosis
· Extraction of vessels, optic disc and fovea avascular zone from fundus fluorescein angiogram based on Hessian analysis of directional curvelet subbands
· Medical image compression with multi-wavelet
· A new adaptive technique for fast and accurate estimation of SSAEP
1. M. Mokhtari, H. Rabbani*, A. Mehridehnavi, R. Kafieh, M. Akhlaghi, M. Pourazizi, L. Fang, "Local comparison of cup to disc ratio in right and left eyes based on fusion of color fundus images and OCT B-scans", Information Fusion, vol. 51, pp. 30-41, 2019.
2. M. Lashgari, M. Shahmoradi, H. Rabbani*, M. Swain, "Missing Surface Estimation Based on Modified Tikhonov Regularization: Application for Destructed Dental Tissue," IEEE Transactions on Image Processing, vol. 27, no.5, pp. 2433-2446, 2018.
3. R. Rasti, H. Rabbani*, A. Mehridehnavi and F. Hajizadeh, "Macular OCT Classification using a Multi-Scale Convolutional Neural Network Ensemble," IEEE Transactions on
Medical Imaging, vol. 37, no. 4, pp. 1024-1034, 2018.
4. A. Rashno, D. D. Koozekanani, P. M. Drayna, B. Nazari, S. Sadri, H. Rabbani, K. K. Parhi*, "Fully-Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema using Neutrosophic Sets and Graph Algorithms," IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 989-1001, May 2018.
5. M.J. Allingham*, D. Mukherjee, E.B. Lally, H. Rabbani, P.S. Mettu, S.W. Cousins, S. Farsiu, "A Quantitative Approach to Predict Differential Effects of Anti-VEGF Treatment on Diffuse and Focal Leakage in Patients with Diabetic Macular Edema - A Pilot Study", Translational Vision Science & Technology, vol. 7, no. 6, 2017.
6. Z. Amini, H. Rabbani*, "Statistical Modeling of Retinal Optical Coherence Tomography", IEEE Transactions on Medical Imaging, vol. 35, no. 6, pp. 1544-1554, 2016.
7. O. Sarrafzadeh, H. Rabbani, A. Mehri Dehnavi*, A. Talebi, "Analyzing Features by SWLDA for the Cassification of HEp-2 Cell Images Using GMM", Pattern Recognition
Letters, vol. 82, part 1, pp. 44-85, Oct. 2016.
8. M. Shahmoradi, M. Lashgari, H. Rabbani*, J. Qin, M. Swain, "A comparative study of new and current methods for dental micro-CT image denoising", Dentomaxillofacial Radiology, vol. 45, no. 3, 2016.
9. M. Niknejad, H. Rabbani*, M. Babaei-Zadeh, "Image Restoration Using Gaussian Mixture Models with Spatially Constrained Patch Clustering," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3624-3636, Nov. 2015.
10. R. Kafieh, H. Rabbani*, I. Selesnick, “Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography”, IEEE Transactions on Medical Imaging, vol. 34, no. 5, pp. 1042-1062, 2015.
11. H. Rabbani*, M.J. Allingham, P.S. Mettu, S.W. Cousins, S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects with Diabetic Macular Edema”,
Investigative Ophthalmology and Visual Science, vol. 56, no. 3, pp. 1482-1492, 2015.
12. MR. Sehhati*, A. Mehri, H. Rabbani, M. Pourhossein, “Stable Gene Signature Selection for Prediction of Breast Cancer Recurrence Using Joint Mutual Information”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 12, no. 6, pp. 1440-1448, 2015.
13. R. Kafieh, H. Rabbani*, M. Sonka, M. D. Abramoff, “Intra-Retinal Layer Segmentation of 3D Optical Coherence Tomography Using Coarse Grained Diffusion Map”, Medical Image Analysis, vol. 17, no. 8, pp. 907-928, Dec. 2013.
14. R. Kafieh, H. Rabbani*, S. Kermani, "A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina", Journal of Medical Signals and Sensors, vol. 3, no. 1, pp. 45-60, 2013.
15. R. Kafieh, H. Rabbani*, F. Hajizadeh, M. Ommani, S. Kermani, “An Accurate Multimodal 3D Vessel Segmentation Method Based on Brightness Variations on OCT Layers and Curvelet Domain Fundus Image Analysis”, IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2815-2823, Oct. 2013.
16. M. Sheikhhosseini, H. Rabbani*, M. Zekri, A. Talebi, “Automatic Diagnosis of Malaria Based on Complete Circle-Ellipse Fitting Search Algorithm”, Journal of Microscopy, vol. 252, no. 3, pp. 189-203, Dec. 2013.
17. M. Esmaeili, H. Rabbani*, A. Mehri, “Automatic Optic Disk Boundary Extraction by the Use of Curvelet Transform and Deformable Variational Level Set Model”, Pattern
Recognition, vol. 47, no. 7, pp. 2832-2842, July 2012.
18. F. Rahimi and H. Rabbani*, “A dual adaptive watermarking scheme in contourlet domain for DICOM images”, BioMedical Engineering OnLine, 10:53, 2011.
19. H. Rabbani, S. Gazor*, R. Nezafat, “Wavelet-Based 3D Medical Image Denoising Using Bivariate Laplacian Mixture Model”, IEEE Transactions on Biomedical Engineering, vol. 56, no. 12, pp.2826-2837, Dec. 2009.
20. H. Rabbani*, “Image Denoising in Steerable Pyramid Domain Based on a Local Laplace Prior”, Pattern Recognition, vol. 42, pp. 2181-2193, Sept. 2009.
21. H. Rabbani, M. Vafadoost, S. Gazor*, P. Abolmaesumi, “Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors”, IEEE
Transactions on Biomedical Engineering, vol. 55, no.9, pp. 2152-2160, Sept. 2008.
Medical Image and Signal Processing (MISP) Research Center is located at the heart of Isfahan University of Medical Sciences. The center was established in 2005 with close collaboration of faculty members from Isfahan University of Medical Sciences and Isfahan university of Technology. At MISP we work on different aspects of biomedical engineering, biomedical image and signal processing. We are dedicated to find new technical solutions for medical devices and fill the gap between the medical and engineering communities. Our missions are:
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The in MISP research center has set one of its goals to develop a reference medical image and signal database for image and signal processing research and development. The main aim of this collection is providing reliable datasets for researchers to compare their algorithms on common framework and to be able to interpret and compare the performance of their newly developed methods. The collected databases are devised to be in accordance with patient privacy and copyright issues. The ground truth is provided for datasets based on evaluation of one, two or three experts. Some datasets are collected from different modalities or from diverse anatomical structures of one patient. Most of the mentioned datasets are available with a published paper in famous journals to demonstrate possible application of the dataset. Please read the following paper about "Isfahan MISP dataset":
Masoud Kashefpoor, Rahele Kafieh, Sahar Jorjandi, Hadis Golmhammadi, Zahra Khodabande, Mohammadreza Abbasi, Nilufar Teifuri, Ali Akbar Fakharzadeh, Maryam Kashefpoor, Hossein Rabbani, "Isfahan MISP Dataset", Journal of Medical Signals and Sensors, vol. 7, no. 1, pp. 43-48, 2017.
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