Segmentation of Optic Disc in Retina Images using Texture

Suraya Mohammad, D. T. Morris, Neil Thacker

Abstract

The paper describes our work on the segmentation of the optic disc in retinal images. Our approach comprises of two main steps; a pixel classification method to identify pixels that may belong to the optic disc boundary and a circular template matching method to estimate the circular approximation of the optic disc boundary. The features used are based on texture, calculated using the intensity differences of local image patches. This was adapted from Binary Robust Independent Elementary Features (BRIEF). BRIEF is inherently invariant to image illumination and has a lower degree of computational complexity compared to other existing texture measurement methods. Fuzzy C-Means (FCM) and Naive Bayes are the clustering and classifier used to cluster/classify the image pixels. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images. The average mean overlap ratio between the true optic disc region and segmented region is 0.81 for both FCM and Naive Bayes. Comparison with a method based on the Hough Transform is also provided.

References

  1. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers.
  2. Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., and Fua, P. (2012). Brief: Computing a local binary descriptor very fast. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(7):1281-1298.
  3. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: binary robust independent elementary features. In Computer Vision-ECCV 2010, pages 778-792. Springer.
  4. Congdon, N. G., Friedman, D. S., and Lietman, T. (2003). Important causes of visual impairment in the world today. JAMA: the journal of the American Medical Association, 290(15):2057-2060.
  5. Cree, M. J., Olson, J. A., McHardy, K. C., Sharp, P. F., and Forrester, J. V. (1999). The preprocessing of retinal images for the detection of fluorescein leakage. Physics in Medicine and Biology, 44(1):293-308. Cited By (since 1996):18.
  6. Duda, R., Hart, P., and Stork, D. (2001). Pattern classification. Wiley, pub-WILEY:adr, second edition.
  7. Foracchia, M., Grisan, E., and Ruggeri, A. (2004). Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Transactions on Medical Imaging, 23(10):1189-1195.
  8. Foracchia, M., Grisan, E., and Ruggeri, A. (2005). Luminosity and contrast normalization in retinal images. Medical Image Analysis, 9(3):179-190.
  9. Grisan, E., Giani, A., Ceseracciu, E., and Ruggeri, A. (2006). Model-based illumination correction in retinal images. In Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on, pages 984-987. IEEE.
  10. Hoover, A. and Goldbaum, M. (2003). Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. Medical Imaging, IEEE Transactions on, 22(8):951-958.
  11. Joshi, G. D. and Sivaswamy, J. (2008). Colour retinal image enhancement based on domain knowledge. In Computer Vision, Graphics & Image Processing, 2008. ICVGIP'08. Sixth Indian Conference on, pages 591- 598. IEEE.
  12. Joshi, G. D., Sivaswamy, J., and Krishnadas, S. (2011). Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. Medical Imaging, IEEE Transactions on, 30(6):1192-1205.
  13. Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: Active Contour Models. International Journal of Computer Vision, 1(4):321-331.
  14. Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., and Kennedy, L. (2004). Optic nerve head segmentation. Medical Imaging, IEEE Transactions on, 23(2):256-264.
  15. May, M. (2008). Automatic Detection of the Optic Disc Within Retinal Images. Master's thesis, University of Manchester, UK.
  16. Morris, D. and Donnison, C. (1999). Identifying the neuroretinal rim boundary using dynamic contours. Image and Vision Computing, 17(3):169-174.
  17. Muramatsu, C., Nakagawa, T., Sawada, A., Hatanaka, Y., Hara, T., Yamamoto, T., and Fujita, H. (2011). Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods. Computer methods and programs in biomedicine, 101(1):23-32.
  18. Ricci, E. and Perfetti, R. (2007). Retinal blood vessel segmentation using line operators and support vector classification. Medical Imaging, IEEE Transactions on, 26(10):1357-1365.
  19. Spencer, T., Olson, J., McHardy, K., Sharp, P., and Forrester, J. (1996). An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers and Biomedical Research, 29(4):284-302.
  20. Tar, P. and Thacker, N. (2011). A quantitative representation for segmentation of martian images. Technical report, ISBE, Medical School, University of Manchester.
  21. Walter, T. and Klein, J.-C. (2002). A computational approach to diagnosis of diabetic retinopathy. In Proceedings of the 6th Conference on Systemics, Cybernetics and Informatics (SCI2002), pages 521-526.
  22. Wang, Y., Tan, W., and Lee, S. C. (2001). Illumination normalization of retinal images using sampling and interpolation. In Medical Imaging 2001, pages 500-507. International Society for Optics and Photonics.
  23. Winder, R., Morrow, P., McRitchie, I., Bailie, J., and Hart, P. (2009). Algorithms for digital image processing in diabetic retinopathy. Computerized Medical Imaging and Graphics, 33(8):608 - 622.
  24. Youssif, A. A., Ghalwash, A. Z., and Ghoneim, A. S. (2007). A comparative evaluation of preprocessing methods for automatic detection of retinal anatomy. In Proceedings of the Fifth International Conference on Informatics and Systems (INFOS 07), pages 24-30.
Download


Paper Citation


in Harvard Style

Mohammad S., T. Morris D. and Thacker N. (2014). Segmentation of Optic Disc in Retina Images using Texture . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 293-300. DOI: 10.5220/0004680802930300


in Bibtex Style

@conference{visapp14,
author={Suraya Mohammad and D. T. Morris and Neil Thacker},
title={Segmentation of Optic Disc in Retina Images using Texture},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={293-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004680802930300},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Segmentation of Optic Disc in Retina Images using Texture
SN - 978-989-758-003-1
AU - Mohammad S.
AU - T. Morris D.
AU - Thacker N.
PY - 2014
SP - 293
EP - 300
DO - 10.5220/0004680802930300