Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images.

Published

Journal Article

Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method.

Full Text

Duke Authors

Cited Authors

  • Chow, YC; Gao, X; Li, H; Lim, JH; Sun, Y; Wong, TY

Published Date

  • 2011

Published In

Volume / Issue

  • 2011 /

Start / End Page

  • 5044 - 5047

PubMed ID

  • 22255472

Pubmed Central ID

  • 22255472

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/IEMBS.2011.6091249

Language

  • eng

Conference Location

  • United States