A computer-aided diagnosis system of nuclear cataract.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Li, H; Lim, JH; Liu, J; Mitchell, P; Tan, AG; Wang, JJ; Wong, TY

Published Date

  • July 2010

Published In

Volume / Issue

  • 57 / 7

Start / End Page

  • 1690 - 1698

PubMed ID

  • 20172776

Pubmed Central ID

  • 20172776

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

Digital Object Identifier (DOI)

  • 10.1109/TBME.2010.2041454

Language

  • eng

Conference Location

  • United States