An automatic diagnosis system of nuclear cataract using slit-lamp images.

Published

Journal Article

An automatic diagnosis system of nuclear cataract is presented 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 (ASM). Based on the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine (SVM) regression is employed to train a grading model for grade prediction. The system is tested using clinical images and clinical ground truth. More than five thousands slit-lamp images were tested. The success rate of feature extraction is 95% and the mean grading difference is 0.36. The automatic diagnosis system can help to improve the grading objectivity and save the workload of ophthalmologists.

Full Text

Duke Authors

Cited Authors

  • Li, H; Lim, JH; Liu, J; Wong, DWK; Tan, NM; Lu, S; Zhang, Z; Wong, TY

Published Date

  • 2009

Published In

Volume / Issue

  • 2009 /

Start / End Page

  • 3693 - 3696

PubMed ID

  • 19965005

Pubmed Central ID

  • 19965005

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/IEMBS.2009.5334735

Language

  • eng

Conference Location

  • United States