Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques

Rajendra Udyavara Acharya, Wenwei Yu, Kuanyi Zhu, Jagadish Nayak, Teik Cheng Lim, Joey Yiptong Chan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

Original languageEnglish
Pages (from-to)619-628
Number of pages10
JournalJournal of Medical Systems
Volume34
Issue number4
DOIs
Publication statusPublished - 01-08-2010
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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