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Automatic detection of retinal vascular landmark features for colour fundus image matching and patient longitudinal study., , , , , and . ICIP, page 616-620. IEEE, (2013)Peripapillary Atrophy Detection by Sparse Biologically Inspired Feature Manifold., , , , , , and . IEEE Trans. Med. Imaging, 31 (12): 2355-2365 (2012)Local patch reconstruction framework for optic cup localization in glaucoma detection., , , , , , , , , and 5 other author(s). EMBC, page 5418-5421. IEEE, (2014)Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Priors., , , , , , and . MICCAI (1), volume 7510 of Lecture Notes in Computer Science, page 58-65. Springer, (2012)Automatic Feature Learning for Glaucoma Detection Based on Deep Learning., , , , , and . MICCAI (3), volume 9351 of Lecture Notes in Computer Science, page 669-677. Springer, (2015)Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis., , , , , , , and . MICCAI (3), volume 6893 of Lecture Notes in Computer Science, page 1-8. Springer, (2011)Focal Biologically Inspired Feature for Glaucoma Type Classification., , , , , , , and . MICCAI (3), volume 6893 of Lecture Notes in Computer Science, page 91-98. Springer, (2011)Sector-based optic cup segmentation with intensity and blood vessel priors., , , , , , , and . EMBC, page 1454-1457. IEEE, (2012)Vessel Segmentation from Color Retinal Images with Varying Contrast and Central Reflex Properties., , , , and . DICTA, page 184-189. IEEE Computer Society, (2010)Learn to recognize pathological myopia in fundus images using bag-of-feature and sparse learning approach., , , , , , and . ISBI, page 888-891. (2013)