Similarity regularized sparse group lasso for cup to disc ratio computation

Jun Cheng, Zhuo Zhang, Dacheng Tao, Damon Wing Kee Wong, Jiang Liu, Mani Baskaran, Tin Aung, Tien Yin Wong

Research output: Journal PublicationArticlepeer-review

20 Citations (Scopus)

Abstract

Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.

Original languageEnglish
Article number#294855
Pages (from-to)3763-3777
Number of pages15
JournalBiomedical Optics Express
Volume8
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Image analysis
  • Image processing
  • Image recognition, algorithms and filters
  • Microscopy

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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