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PhD research intern, MITACS / INRIA Sophia Antipolis
Keywords: Hyperspectral Image Analysis, Subspace Analysis, Latent Structure Models, Feature Selection, Partial Least Squares, Skin Care, Erythema Contact: |
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Abstract:
Ali is involved in a project entitled “Hyperspectral Image Analysis of Skin Erythema after Radiation Therapy”. This project is aimed to use data from a new hyperspectral imaging technology developed at McMaster University in Canada, and to propose relevant image analysis algorithms that can precisely quantify skin erythema and, objectively correlate this phenomenon with radiation response. Acute skin erythema is a common side effect with patients undergoing radiotherapy treatment. It displays itself as an increase in skin redness and irritation. Erythema has been reported to correlate to individual patient response to radiation and therefore may be useful to guide and modify courses of treatment in a timely manner. Currently, upon visual examination, a qualitative score can be assigned to characterize the severity of erythema, which then may be used for assessing radiation response. As this clinical assessment is not a quantitative measure and in fact is a highly subjective one, this project aims to develop an alternative objective measure to accurately quantify skin erythema.
Short Bio:
Ali Madooei received the B.Sc. degree in Computing Science and Artificial Intelligence with high distinction from the Staffordshire University (U.K.) in 2010. He is currently working towards the Ph.D. degree at the School of Computing Science at Simon Fraser University (Canada). His research interests span the areas of Computer Vision, especially with respect to application to Medical Image Analysis. He has been particularly working on incorporating computer vision in dermatology practice. His thesis focuses on early detection of Cutaneous Melanoma with an emphasis on incorporating color features.
