title: Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images.
abstract: 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.
authors.affiliation: National University of Singapore. dart.yc86@gmail.com
authors.forename: Yew Chung
authors.initials: YC
authors.lastname: Chow
authors.forename: Xinting
authors.initials: X
authors.lastname: Gao
authors.forename: Huiqi
authors.initials: H
authors.lastname: Li
authors.forename: Joo Hwee
authors.initials: JH
authors.lastname: Lim
authors.forename: Ying
authors.initials: Y
authors.lastname: Sun
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D002386:Cataract / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008029:Lighting / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity