Here you can find my research/educational activities
IEEE Transactions on Image Processing, 27(5): 2433-2446, 2018.
Currently I am focusing on Statistical & Mathematical Modeling of Medical Signals and Systems. In this base, my field of interests consists of introducing efficient algorithms for biomedical signal analysis and processing including multidimensional data, time-frequency analysis tools including x-lets, denoising and signal/image recovery, and statistical signal processing.Learn MoreDec 01, 2015
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 To create a place for researchers to work on fundamental, applied and interdisciplinary research projects. To communicate with government in order to contribute to society's health To highlight scientific influence of Isfahan University of Medical Sciences in national and international communities. More information are available at http://misprc.ir.Learn MoreMar 14, 2016
You can freely download/upload the datasets from: Isfahan MISP Datasets (biosigdata.com)Learn MoreApr 27, 2018
A Summarized Table about Atomic RepresentationLearn MoreApr 27, 2018
State-of-the-art Method for Image RestorationLearn MoreApr 27, 2018
A Summarized Table about Image ModelingLearn MoreApr 27, 2018
Wiley EEEE Book Chapter doi.org/10.1002/047134608X.W8315
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. A. Rashno, B. Nazari, D. D. Koozekanani, P. M. Drayna, S. Sadri, H. Rabbani, K. K. Parhi, "Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain," PLoS ONE 12(10): e0186949. M. Esmaeili, A. Mehri, H. Rabbani, F. Hajizadeh, “Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based k-SVD ,” Journal of Medical Signals & Sensors, vol. 6, no. 3, pp. 166–171, 2016.
The datasets (24 768*768*x FA videos and late FA images in DME eyes) and manual and automated markings used in the following paper can be downloaded from HERE.
This dataset contains OCT data (in mat format) and color fundus data (in jpg format) of left & right eyes of 50 healthy persons.
This folder contains bone marrow microscopic images. These images are categorized into two groups: Normal Plasma Cells and Myeloma Cells.
We have collected retinal image of 70 patients of different diabetic retinopathy stages including 30 normal data and 40 abnormal data in different stages.
45 24-bit 3264*2448 microscopic images taken from bone marrow samples including leshman bodies.
This dataset contains 260 CT and 202 MR images in DICOM format.
Publicly available database of both fundus fluorescein fngiogram photographs and corresponding color fundus images of 30 healthy persons and 30 patients with diabetic retinopathy.
A set of 2D .mat corneal OCT images of 15 subjects. For example subject#1 includes 41 240×748 B-scans taken from Heidelberg OCT imaging device.
This folder includes 25 colour fundus images of healthy persons and 35 colour fundus images of patients with diabetic retinopathy used for automatic curvelet-based detection of Foveal Avascular Zone (FAZ).
A set of eye images consisting of 22 pairs of images (17 macular and 5 prepapillary), from random patients, each pair acquired from eyes with a variety of retinal diseases. Each image pair includes a colour fundus image and one OCT image acquired with Topcon 3D OCT-1000 instrument. OCT images contain images of 650 different slices with a size of 650 × 512 × 128 voxels and a voxel resolution of 3.125 µm × 3.125 µm × 7 µm.
A dataset for Glomeruli detection was collected with the contribution of MISP Research Center and Department of Pathology at IUMS
This dataset is acquired at Noor Eye Hospital in Tehran and is consisting of 50 normal, 48 dry AMD, and 50 DME OCTs.
7 healthy and 7 glaucoma data captured by Heidelberg Spectralis used to demonstrate the efficacy of a new imaging biomarker namely Volumetric Cup-to-Disc Ratio (VCDR) for diagnosis of ocular diseases such as Glaucoma.