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Proceedings Paper

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework
Author(s): Parag S. Chandakkar; Ragav Venkatesan; Baoxin Li
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Paper Abstract

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

Paper Details

Date Published: 28 February 2013
PDF: 10 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700Q (28 February 2013); doi: 10.1117/12.2008133
Show Author Affiliations
Parag S. Chandakkar, Arizona State Univ. (United States)
Ragav Venkatesan, Arizona State Univ. (United States)
Baoxin Li, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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