A computer-aided diagnosis system of nuclear cataract

Huiqi Li, Joo Hwee Lim, Jiang Liu, Paul Mitchell, Ava Grace Tan, Jie Jin Wang, Tien Yin Wong

Research output: Journal PublicationArticlepeer-review

60 Citations (Scopus)

Abstract

Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on > 5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

Original languageEnglish
Article number5415679
Pages (from-to)1690-1698
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume57
Issue number7
DOIs
Publication statusPublished - Jul 2010
Externally publishedYes

Keywords

  • Automatic grading
  • computer-aided diagnosis
  • nuclear cataract
  • slit lamp image

ASJC Scopus subject areas

  • Biomedical Engineering

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