Abstract
We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in [1], where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular ORIGA dataset, the results show that our approach achieves better performance compared with existing techniques.
Chapter PDF
References
Xu, Y., Liu, J., Lin, S., Xu, D., Cheung, C.Y., Aung, T., Wong, T.Y.: Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Priors. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 58–65. Springer, Heidelberg (2012)
Kingman, S.: Glaucoma is second leading cause of blindness globally. Bull. World Health Organ. 82(11), 887–888 (2004)
Jonas, J., Budde, W., Panda-Jonas, S.: Ophthalmoscopic Evaluation of the Optic Nerve Head. Survey of Ophthalmology 43, 293–320 (1999)
Liu, J., Wong, D.W.K., Lim, J., Li, H., Tan, N.M., Zhang, Z., Wong, T.Y., Lavanya, R.: Argali:an automatic cup-to-disc ratio measurement system for glaucoma analysis using level-set image processing. In: IEEE Int. Conf. Engin. in Med. and Biol. Soc (2008)
Yin, F., Liu, J., Ong, S.H., Sun, D., Wong, D.W.K., Tan, N.M., Baskaran, M., Cheung, C.Y., Aung, T., Wong, T.Y.: Model-based Optic Nerve Head Segmentation on Retinal Fundus Images. In: IEEE Int. Conf. Engin. in Med. and Biol. Soc., pp. 2626–2629 (2011)
Wong, D.W.K., Lim, J.H., Tan, N.M., Zhang, Z., Lu, S., Li, H., Teo, M., Chan, K., Wong, T.Y.: Intelligent Fusion of Cup-to-Disc Ratio Determination Methods for Glaucoma Detection in ARGALI. In: Int. Conf. Engin. in Med. and Biol. Soc., pp. 5777–5780 (2009)
Xu, Y., Xu, D., Lin, S., Liu, J., Cheng, J., Cheung, C.Y., Aung, T., Wong, T.Y.: Sliding Window and Regression based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 1–8. Springer, Heidelberg (2011)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34(11), 2274–2282 (2012)
Cristianini, N., Shawe-Taylor, J., Kandola, J.S.: Spectral kernel methods for clustering. In: Neural Information Processing Systems Conference, NIPS (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 22, 888–905 (2000)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 35(1), 171–184 (2013)
Wang, J., Saligrama, V., Castañón, D.A.: Structural similarity and distance in learning. In: Annual Allerton Conference on Communication, Control, and Computing, Allerton (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Y. et al. (2014). Optic Cup Segmentation for Glaucoma Detection Using Low-Rank Superpixel Representation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_98
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_98
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)