Automatic localization of retinal landmarks

Xiangang Cheng, Damon Wing Kee Wong, Jiang Liu, Beng Hai Lee, Ngan Meng Tan, Jielin Zhang, Ching Yu Cheng, Gemmy Cheung, Tien Yin Wong

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

9 Citations (Scopus)

Abstract

Retinal landmark detection is a key step in retinal screening and computer-aided diagnosis for different types of eye diseases, such as glaucomma, age-related macular degeneration(AMD) and diabetic retinopathy. In this paper, we propose a semantic image transformation(SIT) approach for retinal representation and automatic landmark detection. The proposed SIT characterizes the local statistics of a fundus image and boosts the intrinsic retinal structures, such as optic disc(OD), macula. We propose our salient OD and macular models based on SIT for retinal landmark detection. Experiments on 5928 images show that our method achieves an accuracy of 99.44% in the detection of OD and an accuracy of 93.49% in the detection of macula, while having an accuracy of 97.33% for left and right eye classification. The proposed SIT can automatically detect the retinal landmarks and be useful for further eye-disease screening and diagnosis.

Original languageEnglish
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages4954-4957
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: 28 Aug 20121 Sep 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period28/08/121/09/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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