Automatic Optic Disc Localization Using Particle Swarm Optimization Technique

S. P. Subramanya Jois, S. Harsha, J. R. Harish Kumar

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

2 Citations (Scopus)

Abstract

There is a growing need for plenarily automated algorithms that expeditiously localize the optic disc region in retinal fundus images for the analysis of retinal pathologies such as glaucoma. In this paper, we propose a methodology based on particle swarm optimization for automatic localization of optic disc region from retinal fundus images, where minimization of the fitness function is utilized to resolve optimization quandaries. Here, kernels are modeled as particles and they test the region-of-interest based on the fitness function, in the respective databases, where it is likely that the optic disc exists. The proposed method is validated on a total of 1670 fundus images obtained from various publicly available fundus image datasets. The optic disc localization accuracy obtained by the proposed method are 100%, 98.01%, 96.15%, 98.87%, 100%, and 100% on DRIVE, DRISHTI-GS, DIARETDB0, DIARETDB1, DRIONS-DB, and MESSIDOR fundus image databases, respectively. The precision of localization was improved with initialization of kernel particles within bright region-of-interest in fundus images.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1718-1722
Number of pages5
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 22-02-2019
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 28-10-201831-10-2018

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
CountryKorea, Republic of
CityJeju
Period28-10-1831-10-18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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