Dr. Ehsan Kamrani
Image Processing Mammography Segment Detection Human Vision NCM. electrical engineering biomedical engineering imaging biooptics biomechanical engineering lasers electronics system semiconductor IC design sensors and nanotechnology photonics and optoelectronics computational electronics and photonics Medical Image Segmentation Techniques Image Retrieval and Classification Techniques Digital image processing Analog image processing Microscope image processing Computer Vision and Pattern Recognition Radiology, Nuclear Medicine and Imaging Electrical and Electronic Engineering

Bio

Dr. Ehsan Kamrani is a Research Associate at the Harvard-MIT Division of Health Sciences and Technology and a Scientist at Robarts Research Institute. His affiliations include Harvard University, University of Waterloo, and Harvard Medical School. He completed a Postdoc Research Associate in Material Science and Engineering at Pohang University of Science and Technology (2015) and a Biomedical Engineering in Electrical Engineering degree at Ecole Polytechnique de Montreal (2014). He is a reviewer for GJRE.

Educational Journey

Pohang University of Science and Technology

Postdoc Research Associate in Material Science and Engineering • Material Science and Engineering

2015

Ecole Polytechnique de Montreal

Biomedical Engineering in Electrical Engineering • Electrical Engineering

2014

Experience

Robarts Research Institute

Scientist

2020 - Present • Medicine

2016 - Present

Harvard-MIT Division of Health Sciences and Technology

Research Associate

2012 - Present • Biomedical Engineering
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Editors Role

Reviewer

GJRE

2012 - Present

Research

Wave Prediction and Delay Modeling for Teleoperation via Internet

Article July 10, 2012

This paper propose a novel approach for modeling the end-to-end time delay dynamics of the internet using system identification, and use it for controlling real-time internet-based telerobotic operations. When a single model is used, it needs to adapt to the operating conditions before an appropriate control mechanism can be applied. Slow adaptation may result in large transient errors. As an alternative, we propose to use an adaptive multiple model framework, and determine the best model for the current operating conditions to activate the corresponding controller. We employ multivariable wave prediction method to achieve this objective.

Human Vision Inspired Technique Applied to Detect Suspicious Masses in Mammograms

Article June 20, 2012

Several competitive techniques have been applied for efficient image segmentation and automatic feature extraction through the literatures. There are a lot of open problems and controversial ambiguities regarding to the mechanism which applied by human eye for image segmentation and feature extraction. Here we have first extracted the human vision technique applied for image segmentation and we have implemented this technique for automatic image segmentation and feature extraction. The features have been categorized into the internal and external modalities. We have introduced the negative curvature minima (NCM) points as a dominant external feature and the textures detected using pulse coupled neural networks (PCNNs) and LAWs methods as the dominant internal feature used by human vision to segment and extracts the features of an image. These features have been used to detect suspicious masses in mammogram images using the proposed human eye inspired technique. The results justify the efficiency of the proposed method.