Table of Contents
Journal of Medical Engineering
Volume 2013, Article ID 408120, 21 pages
http://dx.doi.org/10.1155/2013/408120
Research Article

Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

1Department of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences, P.O. Nilore, Islamabad 45650, Pakistan
2Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, P.O. Nilore, Islamabad 45650, Pakistan

Received 30 August 2013; Revised 1 November 2013; Accepted 18 November 2013

Academic Editor: Hasan Al-Nashash

Copyright © 2013 Asloob Ahmad Mudassar and Saira Butt. 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. A. Hoover, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Ege, O. Larsen, and O. Hejlesen, “Detection of abnormalities in retinal images using digital image analysis,” in Proceedings of the 11th Scandinavian Conference on image Processing, pp. 833–840, 1999.
  3. C. Sinthanayothin, Image analysis for Automatic diagnosis of diabetic retinopathy [Ph.D. thesis], Kings College London, 1999.
  4. A. Hoover and M. Goldbaum, “Illumination Equalization of a Retinal image Using the Blood Vessels as a Reference,” Abstract in the annual meeting of the Association for Research in Vision and Ophthalmology (ARVO), 2001.
  5. W. E. Hart, M. Goldbaum, B. Cote, P. Kube, and M. R. Nelson, “Automated measurement of retinal vascular tortuosity,” in Proceedings of the AMIA Fall Conference, 1997.
  6. G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” British Journal of Ophthalmology, vol. 80, no. 11, pp. 940–944, 1996. View at Google Scholar · View at Scopus
  7. 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 Google Scholar · View at Scopus
  8. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, 2001.
  9. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269, 1989. View at Publisher · View at Google Scholar · View at Scopus
  10. D. H. Friedman, Detection of Signals by Template Matching, Johns Hopkins University Press, Baltimore, Md, USA, 1969.
  11. http://www.ces.clemson.edu/~ahoover/stare/.
  12. A. Hoover and M. Goldbaum, “Illumination Equalization of a Retinal image Using the Blood Vessels as a Reference,” Abstract in the annual meeting of the Association for Research in Vision and Ophthalmology (ARVO), 2001.
  13. L. Xu and S. Luo, “A novel method for blood vessel detection from retinal images,” BioMedical Engineering Online, vol. 9, article 14, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. W. L. Yun, U. Rajendra Acharya, Y. V. Venkatesh, C. Chee, L. C. Min, and E. Y. K. Ng, “Identification of different stages of diabetic retinopathy using retinal optical images,” Information Sciences, vol. 178, no. 1, pp. 106–121, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. A. R. Mohammad, Q. Munib, and A. Muhammad, “An improved matched filter for blood vessel detection of digital retinal images,” Computers in Biology and Medicine, vol. 37, no. 2, pp. 262–267, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, A. A. Bharath, and K. H. Parker, “Segmentation of blood vessels from red-free and fluorescein retinal images,” Medical Image Analysis, vol. 11, no. 1, pp. 47–61, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. F. MVC and E. MFR, “Fast automatic retinal vessel segmentation and vascular landmarks extraction method for biometric applications,” in IEEE International Conference on Biometrics, Identity and Security (BIdS '09), Tampa, Fla, USA, September 2009.
  18. N. Otsu, “A threshold selection method from gray level histograms,” IEEE Transaction on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Google Scholar · View at Scopus
  19. T. Chanwimaluang and G. Fan, “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding,” in Proceedings of the IEEE International Symposium on Circuits and Systems, pp. V21–V24, May 2003. View at Scopus
  20. A. Hunter, J. Lowell, D. Steel, A. Basu, and R. Ryder, “Non-linear filtering for vascular segmentation and detection of venous beading,” Technical Report, University of Durham, 2003. View at Google Scholar
  21. T. Chanwimaluang, G. Fan, and S. R. Fransen, “Hybrid retinal image registration,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 129–142, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Wu, M. Zhang, J.-C. Liu, and W. Bauman, “On the adaptive detection of blood vessels in retinal images,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 2, pp. 341–343, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. http://www.vcipl.okstate.edu/localentropy.htm.
  24. M. Ashoorirad and R. Baghbani, “Blood vessel segmentation in angiograms using fuzzy inference system and mathematical morphology,” in Proceedings of the International Conference on Signal Processing Systems (ICSPS '09), pp. 272–276, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. M. U. Akram, A. Atzaz, S. F. Aneeque, and S. A. Khan, “Blood vessel enhancement and segmentation using wavelet transform,” in Proceedigs of the International Conference on Digital Image Processing, pp. 34–38, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Yao and H.-J. Chen, “Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm,” Journal of Central South University of Technology, vol. 16, no. 4, pp. 640–646, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. H. F. Jelinek, M. J. Cree, J. J. G. Leandro, J. V. B. Soares, R. M. Cesar Jr., and A. Luckie, “Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy,” Journal of the Optical Society of America A, vol. 24, no. 5, pp. 1448–1456, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. E. Doǧanay, S. Kara, O. Toker, A. Güven, and A. Ö. Öner, “Extraction of blood vessel centerlines on retinal images,” in Proceedings of the 15th National Biomedical Engineering Meeting (BIYOMUT '10), April 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Yang, S. Huang, and N. Rao, “An automatic hybrid method for retinal blood vessel extraction,” International Journal of Applied Mathematics and Computer Science, vol. 18, no. 3, pp. 399–407, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. M. U. Akram and S. A. Khan, “Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy,” Engineering with Computers, vol. 29, no. 2, pp. 165–173, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. M. R. K. Mookiah, U. Rajendra Acharya, C. K. Chua et al., “Computer-aided diagnosis of diabetic retinopathy,” Computers in Biology and Medicine, vol. 43, no. 12, pp. 2136–2155, 2013. View at Google Scholar
  32. R. J. Vidmar, “On the use of atmospheric plasmas as electromagnetic reflectors,” IEEE Transaction on Plasma Science, vol. 21, no. 3, pp. 876–880, 1992. View at Google Scholar
  33. A. A. Mudassar and S. Butt, “Snakes with coordinate regeneration technique: an application to retinal disc boundary detection,” Journal of Medical Engineering, vol. 2013, Article ID 852613, 11 pages, 2013. View at Publisher · View at Google Scholar
  34. A. A. Mudassar and S. Butt, “Application of principal component analysis in automatic localization of optic disc and fovea in retinal images,” Journal of Medical Engineering, vol. 2013, Article ID 989712, 12 pages, 2013. View at Publisher · View at Google Scholar