Automatic Segmentation of Fovea and Macula in Retinal Fundus Images

B. Vaibhav Mallya, J. H. Gagan, Garvit Chhabra, Yogish S. Kamath, Neetha I.R. Kuzhuppilly, J. R.Harish Kumar

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

Abstract

We propose an automated method to segment the fovea and macular region in retinal fundus images which is a precursor to assessing the severity of age-related macular degeneration. The fovea region has been localized using the zero-mean normalized cross-correlation technique. The segmentation of the fovea and the macular region has been achieved using the elliptical active disc technique. The elliptical active disc is a shape-specific active contour model and has five free parameters. To obtain the optimal active disc fit on the region of interest to be segmented, the active disc energy has been optimized with respect to five free parameters using the gradient ascent algorithm. Further computational savings has been achieved using Green's theorem. The experimental validation has been done on MESSIDOR, DIARETDB0, DIARETDB1, DRIVE, and IDRiD fundus image databases adding up to 508 images for segmentation of fovea. We attain an average Dice similarity index and fovea segmentation accuracy of 80.45% and 99.66%, respectively on varied fundus image data.

Original languageEnglish
Title of host publicationProceedings of 2022 IEEE Region 10 International Conference, TENCON 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450959
DOIs
Publication statusPublished - 2022
Event2022 IEEE Region 10 International Conference, TENCON 2022 - Virtual, Online, Hong Kong
Duration: 01-11-202204-11-2022

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2022-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2022 IEEE Region 10 International Conference, TENCON 2022
Country/TerritoryHong Kong
CityVirtual, Online
Period01-11-2204-11-22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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