A-levelset-based automatic cup-to-disc ratio measurement for glaucoma diagnosis from fundus image

Jiang Liu, Fengshou Yin, Damon Wing Kee Wong, Zhuo Zhang, Ngan Meng Tan, Carol Cheung, Mani Baskaran, Tin Aung, Tien Yin Wong

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

4 Citations (Scopus)

Abstract

To boost the performance of level set algorithms, we propose the A-Levelset algorithm, which cascades the level set and active shape model (ASM). The A-Levelset-based ARGALI system is built to automatically segment the optic cup and optic disc from 2D digital fundus images. The ARGALI system further calculates the cup-to-disc ratio (CDR), which is an important indicator in glaucoma assessment and diagnosis. The ARGALI system was tested on a large clinical image collection of 2616 patients in order to estimate the CDR values. The extensive experimental results clearly show that ARGALI outperforms the level set-based approach by reducing the mean absolute error rate of CDR measurement from 0.349 to 0.21 and the mean square error rate from 0.156 to 0.07. ARGALI demonstrates for the first time the capability of automatic CDR measurement in a large clinical data set. It paves the way for automatic objective glaucoma diagnosis and screening using widely available fundus images.

Original languageEnglish
Title of host publicationImage Analysis and Modeling in Ophthalmology
PublisherCRC Press
Pages129-142
Number of pages14
ISBN (Electronic)9781466559387
ISBN (Print)9781466559301
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

ASJC Scopus subject areas

  • Engineering (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Medicine (all)

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