Automatic opacity detection in retro-illumination images for cortical cataract diagnosis

Huiqi Li, Liling Ko, Joo Hwee Lim, Jiang Liu, Damon Wing Kee Wong, Tien Yin Wong, Ying Sun

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Computer aided analysis of medical images, a unique type of non-text media, can facilitate clinical diagnosis. As an example, an automatic opacity detection approach is proposed in this paper to grade cortical cataract more objectively. The automatic pupil detection is performed by detecting the strongest edges on the convex hull and ellipse fitting using nonlinear least square method. The cortical opacity is detected by radial edge detection and postprocessing. The automatic grades are assigned following Wisconsin cataract grading protocol. The accuracy of pupil detection is 98.2%. The mean error of opacity area detection is 7 percent compared with the result of human grader. And 86.3% accurate grades of cortical cataract are achieved. This is the first time that the spoke-like feature is utilized in the automatic detection of cortical cataract to separate from other opacity types. The encouraging results show that it is probable to apply the proposed approach to clinical diagnosis later.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings
Pages553-556
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Hannover, Germany
Duration: 23 Jun 200826 Jun 2008

Publication series

Name2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings

Conference

Conference2008 IEEE International Conference on Multimedia and Expo, ICME 2008
Country/TerritoryGermany
CityHannover
Period23/06/0826/06/08

Keywords

  • Cortical cataract
  • Medical image
  • Opacity detection

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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