TY - GEN
T1 - Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression
AU - Xu, Yanwu
AU - Gao, Xinting
AU - Lin, Stephen
AU - Wong, Damon Wing Kee
AU - Liu, Jiang
AU - Xu, Dong
AU - Cheng, Ching Yu
AU - Cheung, Carol Y.
AU - Wong, Tien Yin
PY - 2013
Y1 - 2013
N2 - 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 (ε) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error ≤ 0.5 (Re0.5), and a 98.9% decimal grading error ≤ 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.
AB - 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 (ε) of 0.336, a 69.0% exact integral agreement ratio (R0), a 85.2% decimal grading error ≤ 0.5 (Re0.5), and a 98.9% decimal grading error ≤ 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.
UR - http://www.scopus.com/inward/record.url?scp=84885902033&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40763-5_58
DO - 10.1007/978-3-642-40763-5_58
M3 - Conference contribution
C2 - 24579174
AN - SCOPUS:84885902033
SN - 9783642407628
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 468
EP - 475
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -
