TY - GEN
T1 - Domain prior based superpixel propagation for optic cup localization
AU - Tan, Ngan Meng
AU - Xu, Yanwu
AU - Liu, Jiang
AU - Goh, Wooi Boon
AU - Cheung, Carol
AU - Aung, Tin
AU - Wong, Tien Yin
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In this paper, we present an unsupervised framework using domain priors extracted from the primary structures of the optic nerve head for automated optic cup localization. Our approach provides 3 major contributions. First, we identify a new domain prior, optic cup origin. This prior is derived from the physiological understanding that the central retinal vessels traces its origin from the optic cup before extending to the rest of the retinal. Second, we propose extracting the features of the optic nerve head from superpixels, which are obtained from low-level grouping and have more natural and descriptive features than pixel based techniques. Third, the domain knowledge comprising of optic cup origin and cup pallor, and the extracted features from superpixels are then used to drive a similarity-based label propagation and refinement scheme for the optic cup localization. Our approach was validated on a clinical online dataset, ORIGA-light, of 650 population-based images. Overall, our approach is able to achieve a 32.2% non-overlap ratio (m1), a 33.8% relative absolute area difference (m2) and a 10.6% absolute CDR error (δ).
AB - In this paper, we present an unsupervised framework using domain priors extracted from the primary structures of the optic nerve head for automated optic cup localization. Our approach provides 3 major contributions. First, we identify a new domain prior, optic cup origin. This prior is derived from the physiological understanding that the central retinal vessels traces its origin from the optic cup before extending to the rest of the retinal. Second, we propose extracting the features of the optic nerve head from superpixels, which are obtained from low-level grouping and have more natural and descriptive features than pixel based techniques. Third, the domain knowledge comprising of optic cup origin and cup pallor, and the extracted features from superpixels are then used to drive a similarity-based label propagation and refinement scheme for the optic cup localization. Our approach was validated on a clinical online dataset, ORIGA-light, of 650 population-based images. Overall, our approach is able to achieve a 32.2% non-overlap ratio (m1), a 33.8% relative absolute area difference (m2) and a 10.6% absolute CDR error (δ).
UR - http://www.scopus.com/inward/record.url?scp=84881630838&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2013.6556616
DO - 10.1109/ISBI.2013.6556616
M3 - Conference contribution
AN - SCOPUS:84881630838
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 880
EP - 883
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
T2 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -