TY - GEN
T1 - Efficient optic cup localization using regional propagation based on retinal structure priors
AU - Xu, Yanwu
AU - Liu, Jiang
AU - Cheng, Jun
AU - Yin, Fengshou
AU - Tan, Ngan Meng
AU - Wong, Damon Wing Kee
AU - Baskaran, Mani
AU - Cheng, Ching Yu
AU - Wong, Tien Yin
PY - 2012
Y1 - 2012
N2 - We present a regional propagation approach based on retinal structure priors to localize the optic cup in 2D fundus images, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to more descriptive and effective features than those employed by pixel based techniques, without additional computational cost. Second, the proposed approach does not need manually labeled training samples, but uses the structural priors on relative cup and disc positions. Third, a refinement scheme that utilizes local context information is adopted to further improve the accuracy. Tested on the ORIGA-light clinical dataset, which comprises of 325 images from a population-based study, the proposed method achieves a 34.9% non-overlap ratio with manually-labeled ground-truth and a 0.104 absolute cup-to-disc ratio (CDR) error. This level of accuracy is much higher than the state-of-the-art pixel based techniques, with a comparable or even less computational cost.
AB - We present a regional propagation approach based on retinal structure priors to localize the optic cup in 2D fundus images, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to more descriptive and effective features than those employed by pixel based techniques, without additional computational cost. Second, the proposed approach does not need manually labeled training samples, but uses the structural priors on relative cup and disc positions. Third, a refinement scheme that utilizes local context information is adopted to further improve the accuracy. Tested on the ORIGA-light clinical dataset, which comprises of 325 images from a population-based study, the proposed method achieves a 34.9% non-overlap ratio with manually-labeled ground-truth and a 0.104 absolute cup-to-disc ratio (CDR) error. This level of accuracy is much higher than the state-of-the-art pixel based techniques, with a comparable or even less computational cost.
UR - http://www.scopus.com/inward/record.url?scp=84870816518&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6346208
DO - 10.1109/EMBC.2012.6346208
M3 - Conference contribution
C2 - 23366169
AN - SCOPUS:84870816518
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1430
EP - 1433
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -