Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

Parag S. Chandakkar; Ragav Venkatesan; Baoxin Li

doi:10.1117/12.2008133

28 February 2013 Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

Parag S. Chandakkar, Ragav Venkatesan, Baoxin Li

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700Q (2013) https://doi.org/10.1117/12.2008133
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Parag S. Chandakkar, Ragav Venkatesan, and Baoxin Li "Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700Q (28 February 2013); https://doi.org/10.1117/12.2008133

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