title: A computer-aided diagnosis system of nuclear cataract.
abstract: Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated 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. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore. huiqili@i2r.a-star.edu.sg
authors.forename: Huiqi
authors.initials: H
authors.lastname: Li
authors.forename: Joo Hwee
authors.initials: JH
authors.lastname: Lim
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.forename: Paul
authors.initials: P
authors.lastname: Mitchell
authors.forename: Ava Grace
authors.initials: AG
authors.lastname: Tan
authors.forename: Jie Jin
authors.initials: JJ
authors.lastname: Wang
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002386:Cataract / Q000175:diagnosis* / Q000473:pathology; D018450:Disease Progression; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results