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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 6814791, 10 pages
http://dx.doi.org/10.1155/2016/6814791
Research Article

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

1Department of Computer Engineering, Karabük University, 78050 Karabük, Turkey
2Department of Computer Engineering, Yıldırım Beyazıt University, 06030 Ankara, Turkey

Received 25 November 2015; Revised 6 March 2016; Accepted 9 March 2016

Academic Editor: Po-Hsiang Tsui

Copyright © 2016 Kemal Akyol et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” British Journal of Ophthalmology, vol. 83, no. 8, pp. 902–910, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Walter and J. C. Klein, “Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques,” in Medical Data Analysis: Second International Symposium, ISMDA 2001 Madrid, Spain, October 8-9, 2001 Proceedings, vol. 2199 of Lecture Notes in Computer Science, pp. 282–287, Springer, Berlin, Germany, 2001. View at Publisher · View at Google Scholar
  3. M. Park, J. S. Jin, and S. Luo, “Locating the optic disc in retinal images,” in Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (CGIV '06), pp. 141–145, IEEE, Sydney, Australia, July 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Lalonde, M. Beaulieu, and L. Gagnon, “Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching,” IEEE Transactions on Medical Imaging, vol. 20, no. 11, pp. 1193–1200, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. C. A. Perez, D. A. Schulz, C. M. Aravena, C. I. Perez, and V. T. Juan, “A new method for online retinal optic-disc detection based on cascade classifiers,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC '13), pp. 4300–4304, IEEE, Manchester, UK, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Comparison of colour spaces for optic disc localisation in retinal images,” in Proceedings of the 16th International Conference on Pattern Recognition, vol. 1, pp. 743–746, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Lowell, A. Hunter, D. Steel et al., “Optic nerve head segmentation,” IEEE Transactions on Medical Imaging, vol. 23, no. 2, pp. 256–264, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Esmaeili, H. Rabbani, A. M. Dehnavi, and A. Dehghani, “Automatic detection of exudates and optic disk in retinal images using curvelet transform,” IET Image Processing, vol. 6, no. 7, pp. 1005–1013, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. N. C. Mithun, S. Das, and S. A. Fattah, “Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique,” in Proceedings of the 16th International Conference on Computer and Information Technology (ICCIT '14), pp. 98–102, IEEE, Khulna, Bangladesh, March 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. Vahabi, M. Vafadoost, and S. Gharibzadeh, “The new approach to automatic detection of optic disc from non-dilated retinal images,” in Proceedings of the 17th Iranian Conference in Biomedical Engineering (ICBME '10), Isfahan, Iran, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. M. R. S. P. Kumara and R. G. N. Meegama, “Active contour-based segmentation and removal of optic disk from retinal images,” in Proceedings of the International Conference on Advances in ICT for Emerging Regions (ICTer '13), pp. 15–20, Colombo, Sri Lanka, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Dhiravidachelvi and V. Rajamani, “Computerized detection of optic disc in diabetic retinal images using background subtraction model,” in Proceedings of the International Conference on Circuits, Power and Computing Technologies (ICCPCT '14), pp. 1217–1222, IEEE, Nagercoil, India, March 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Xu, O. Chutatape, and P. Chew, “Automated optic disk boundary detection by modified active contour model,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 3, pp. 473–482, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Zubair, A. Yamin, and S. A. Khan, “Automated detection of Optic Disc for the analysis of retina using color fundus image,” in Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '13), pp. 239–242, Beijing, China, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Kavitha and S. Shenbaga Devi, “Automatic detection of optic disc and exudates in retinal images,” in Proceedings of the 2nd International Conference on Intelligent Sensing and Information Processing (ICISIP '05), pp. 501–506, January 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Azam, M. U. Akram, and U. Qamar, “Optic disc segmentation from colored retinal images using vessel density,” in Proceedings of the 12th International Conference on Frontiers of Information Technology (FIT '14), pp. 313–318, Islamabad, Pakistan, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Lu and J. H. Lim, “Automatic optic disc detection through background estimation,” in Proceedings of the 17th IEEE International Conference on Image Processing, pp. 833–836, IEEE, Hong Kong, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. D. A. Godse and D. S. Bormane, “Automated localization of optic disc in retinal images,” International Journal of Advanced Computer Science and Applications, vol. 4, no. 2, pp. 65–71, 2013. View at Publisher · View at Google Scholar
  19. C. A. Lupascu, D. Tegolo, and L. D. Rosa, “Automated detection of optic disc location in retinal images,” in Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS '08), pp. 17–22, IEEE, Jyvaskyla, Finland, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Sekhar, W. A. Nuaimy, and A. K. Nandi, “Automated localization of optic disc and fovea in retinal fundus images,” in Proceedings of the 16th European Signal Processing Conference (EUSIPCO '08), pp. 25–29, Lausanne, Switzerland, August 2008.
  21. R. J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto, and A. Hajdu, “Combining algorithms for automatic detection of optic disc and macula in fundus images,” Computer Vision and Image Understanding, vol. 116, no. 1, pp. 138–145, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Ying, M. Zhang, and J. C. Liu, “Fractal-based automatic localization and segmentation of optic disc in retinal images,” in Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 4139–4141, 2007.
  23. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. Y.-G. Jiang, C.-W. Ngo, and J. Yang, “Towards optimal bag-of-features for object categorization and semantic video retrieval,” in Proceedings of the 6th ACM International Conference on Image and Video Retrieval (CIVR '07), pp. 494–501, Amsterdam, The Netherlands, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: spatial pyramid matching for recognizing natural scene categories,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), pp. 2169–2178, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 524–531, IEEE, June 2005. View at Scopus
  28. J. Sivic and A. Zisserman, “Video google: a text retrieval approach to object matching in videos,” in Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477, Nice, France, October 2003. View at Scopus
  29. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, “Local binary patterns and its application to facial image analysis: a survey,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 41, no. 6, pp. 765–781, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. K. K. Umesh and Suresha, “Web image retrieval using visual dictionary,” International Journal on Web Service Computing, vol. 3, no. 3, pp. 77–84, 2012. View at Publisher · View at Google Scholar
  33. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  34. C. C. Aggarwal, Data Classification: Algorithms and Applications, Data Mining and Knowledge Discovery Series, CRC Press, 2014, Edited by V. Kumar.
  35. Ö. Akar and O. Güngör, “Classification of multispectral images using Random Forest algorithm,” Journal of Geodesy and Geoinformation, vol. 1, no. 2, pp. 105–112, 2012. View at Publisher · View at Google Scholar