Automatic notch detection in retinal images

Mei Hui Tan, Ying Sun, Sim Heng Ong, Jiang Liu, Mani Baskaran, Tin Aung, Tien Yin Wong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


This paper presents a new method to detect notching in the optic cup using retinal images. Optic cup notching is an important feature in differentiating normal from glaucomatous eyes. The proposed notching detection method comprises four steps: disc and vessel segmentation, vessel bend detection at key regions, feature points selection and automatic classification. The key step of vessel bend detection involves computing the local curvature of the vessels, then ranking them based on the angle of vessel bend and the local gradient in the neighborhood region. The algorithm was tested on a set of color fundus images and achieved a notching detection rate of 88.9%, a false alarm rate of 4.0%, and an overall accuracy of 95.4%.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Number of pages4
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 7 Apr 201311 Apr 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA


  • glaucoma
  • notch detection
  • optic cup
  • retina
  • vessel curvature

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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