title: Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression.
abstract: Cataracts, which result from lens opacification, are the leading cause of blindness worldwide. Current methods for determining the severity of cataracts are based on manual assessments that may be weakened by subjectivity. In this work, we propose a system to automatically grade the severity of nuclear cataracts from slit-lamp images. We introduce a new feature for cataract grading together with a group sparsity-based constraint for linear regression, which performs feature selection, parameter selection and regression model training simultaneously. In experiments on a large database of 5378 images, our system outperforms the state-of-the-art by yielding with respect to clinical grading a mean absolute error (epsilon) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error < or = 0.5 (Re0.5), and a 98.9% decimal grading error < or = 1.0 (Re1.0). Through a more objective grading of cataracts using our proposed system, there is potential for better clinical management of the disease.
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore.
authors.forename: Yanwu
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore.
authors.forename: Xinting
authors.initials: X
authors.lastname: Gao
authors.affiliation: Microsoft Research Asia, PR China.
authors.forename: Stephen
authors.initials: S
authors.lastname: Lin
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore.
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore.
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore.
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
authors.affiliation: Singapore Eye Research Institute, Singapore.
authors.forename: Ching-Yu
authors.initials: CY
authors.lastname: Cheng
authors.affiliation: Singapore Eye Research Institute, Singapore.
authors.forename: Carol Y
authors.initials: CY
authors.lastname: Cheung
authors.affiliation: Singapore Eye Research Institute, Singapore.
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002386:Cataract / Q000473:pathology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008029:Lighting / Q000379:methods; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index