title: Image based diagnosis of cortical cataract.
abstract: An automatic approach to detect cortical opacities and grade the severity of cortical cataract from retro-illumination images is proposed. The spoke-like feature of cortical opacity is employed to separate from other opacity type. The proposed algorithms were tested by images from a community study. The success rate of region of interest (ROI) detection is 98.2% for 611 images. For 466 images tested, the mean error of opacity area detection is 3.15% compared with human grader and 85.6% of exact cortical cataract grading is obtained. The experimental results show that the proposed approach is promising in clinical diagnosis.
authors.affiliation: Institute for Infocomm Research, A*STAR, 21 Heng Mui Keng Terrace, Singapore. huiqili@i2r.a-star.edu.sg
authors.forename: Huiqi
authors.initials: H
authors.lastname: Li
authors.forename: Liling
authors.initials: L
authors.lastname: Ko
authors.forename: Joo Hwee
authors.initials: JH
authors.lastname: Lim
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D002386:Cataract / Q000145:classification / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000592:standards*; D007908:Lens, Crystalline / Q000033:anatomy & histology / Q000473:pathology*; D015588:Observer Variation; D011680:Pupil / Q000502:physiology; D015203:Reproducibility of Results; D014792:Visual Acuity / Q000502:physiology