An automatic diagnosis system of nuclear cataract using slit-lamp images.
Journal Article (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.
- Li, H; Lim, JH; Liu, J; Wong, DWK; Tan, NM; Lu, S; Zhang, Z; Wong, TY
- Annu Int Conf Ieee Eng Med Biol Soc
Volume / Issue
- 2009 /
Start / End Page
- 3693 - 3696
International Standard Serial Number (ISSN)
Digital Object Identifier (DOI)
- United States