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

Parag S. Chandakkar, Ragav Venkatesan, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationComputer-Aided Diagnosis
DOIs
StatePublished - Jun 5 2013
EventMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8670
ISSN (Print)0277-786X

Other

OtherMedical Imaging 2013: Computer-Aided Diagnosis
CountryUnited States
CityLake Buena Vista, FL
Period2/12/132/14/13

Keywords

  • Color correlogram
  • Diabetic retinopathy
  • Fast radial symmetric transform
  • Image retrieval
  • Multiple-instance learning
  • Steerable Gaussian filters

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Chandakkar, P. S., Venkatesan, R., & Li, B. (2013). Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework. In Medical Imaging 2013: Computer-Aided Diagnosis [86700Q] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8670). https://doi.org/10.1117/12.2008133