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

Annu Int Conf IEEE Eng Med Biol Soc. 2009:2009:3693-6. doi: 10.1109/IEMBS.2009.5334735.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Automation
  • Cataract / diagnosis*
  • Cataract / pathology*
  • Diagnosis, Computer-Assisted
  • Diagnostic Imaging / methods
  • Diagnostic Techniques, Ophthalmological*
  • Electronic Data Processing*
  • Humans
  • Lens, Crystalline / pathology
  • Regression Analysis
  • Reproducibility of Results
  • Software