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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 58–65Cite as

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Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Priors

Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Priors

  • Yanwu Xu19,
  • Jiang Liu19,
  • Stephen Lin20,
  • Dong Xu21,
  • Carol Y. Cheung22,
  • Tin Aung22,23 &
  • …
  • Tien Yin Wong22,23 
  • Conference paper
  • 5658 Accesses

  • 23 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

We present a superpixel based learning framework based on retinal structure priors for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, 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 features more descriptive and effective than those employed by pixel-based techniques, while yielding significant computational savings over methods based on sliding windows. Second, the classifier learning process does not rely on pre-labeled training samples, but rather the training samples are extracted from the test image itself using structural priors on relative cup and disc positions. Third, we present a classification refinement scheme that utilizes both structural priors and local context. Tested on the ORIGA − light clinical dataset comprised of 650 images, the proposed method achieves a 26.7% non-overlap ratio with manually-labeled ground-truth and a 0.081 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. This level of accuracy is comparable to or higher than the state-of-the-art technique [1], with a speedup factor of tens or hundreds.

Keywords

  • Fundus Image
  • Linear Support Vector Machine
  • Support Vector Machine Training
  • Disc Image
  • Glaucoma Diagnosis

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work is funded by Singapore A*STAR SERC Grant (092-148-00731).

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References

  1. 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)

    CrossRef  Google Scholar 

  2. Klein, B., Klein, R., Sponsel, W., Franke, T., Cantor, L., Martone, J., Menage, M.: Prevalence of glaucoma: the beaver dam eye study. Ophthalmology 99(10), 1499–1504 (1992)

    Google Scholar 

  3. Foster, P., Oen, F., Machin, D., Ng, T., Devereux, J., Johnson, G., Khaw, P., Seah, S.: The prevalence of glaucoma in Chinese residents of Singapore: a cross-sectional population survey of the Tanjong Pagar district. Arch Ophthalmology 118(8), 1105–1111 (2000)

    Google Scholar 

  4. Shen, S., Wong, T.Y., Foster, P., Loo, J., Rosman, M., Loon, S., Wong, W., Saw, S.M., Aung, T.: The prevalence and types of glaucoma in Malay people: the Singapore Malay eye study. Invest Ophthalmol. Vis. Sci. 49(9), 3846–3851 (2008)

    CrossRef  Google Scholar 

  5. Jonas, J., Budde, W., Panda-Jonas, S.: Ophthalmoscopic evaluation of the optic nerve head. Survey of Ophthalmology 43, 293–320 (1999)

    CrossRef  Google Scholar 

  6. Abramoff, M., Alward, W., Greenlee, E., Shuba, L., Kim, C., Fingert, J., Kwon, Y.: Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007)

    CrossRef  Google Scholar 

  7. Liu, J., Wong, D.W.K., Lim, J.H., 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: Int. Conf. Biomed. Eng. (2008)

    Google Scholar 

  8. Li, C., Xu, C., Gui, C., Fox, M.: Level set evolution without re-initialization: A new variational formulation. In: CVPR, pp. 430–436 (2005)

    Google Scholar 

  9. Merickel, M., Wu, X., Sonka, M., Abramoff, M.: Optimal segmentation of the optic nerve head from stereo retinal images. In: Med. Imag.: Phys., Func., and Struct. from Med. Im. (2006)

    Google Scholar 

  10. 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. 5777–8570 (2009)

    Google Scholar 

  11. Zhang, Z., Yin, F., Liu, J., Wong, D.W.K., Tan, N.M., Lee, B.H., Cheng, J., Wong, T.Y.: Origa-light: An online retinal fundus image database for glaucoma analysis and research. In: IEEE Int. Conf. Engin. in Med. and Biol. Soc., pp. 3065–3068 (2010)

    Google Scholar 

  12. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC Superpixels. EPFL Technical report (2010)

    Google Scholar 

  13. Ren, C.Y., Reid, I.: gSLIC: a real-time implementation of SLIC superpixel segmentation. Technical report. University of Oxford, Department of Engineering Science (2011)

    Google Scholar 

  14. Onkaew, D., Turior, R., Uyyanonvara, B., Akinori, N., Sinthanayothin, C.: Automatic Vessel Extraction with combined Bottom-hat and Matched-filter. In: Int. Conf. Information and Communication Technology for Embedded Systems (ICICTES), pp. 101–105 (2011)

    Google Scholar 

  15. Tighe, J., Lazebnik, S.: SuperParsing: Scalable Nonparametric Image Parsing with Superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  16. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore

    Yanwu Xu & Jiang Liu

  2. Microsoft Research Asia, P.R. China

    Stephen Lin

  3. School of Computer Engineering, Nanyang Technological University, Singapore

    Dong Xu

  4. Singapore Eye Research Institute, Singapore

    Carol Y. Cheung, Tin Aung & Tien Yin Wong

  5. Department of Ophthalmology, National University of Singapore, Singapore

    Tin Aung & Tien Yin Wong

Authors
  1. Yanwu Xu
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  2. Jiang Liu
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  3. Stephen Lin
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  4. Dong Xu
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  5. Carol Y. Cheung
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  6. Tin Aung
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  7. Tien Yin Wong
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Xu, Y. et al. (2012). Efficient Optic Cup Detection from Intra-image Learning with Retinal Structure Priors. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_8

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  • Print ISBN: 978-3-642-33414-6

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