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An Enhanced Segmentation Technique for Blood Vessel in Retinal Images

by Kimmy Mehta, Navpreet Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 6
Year of Publication: 2016
Authors: Kimmy Mehta, Navpreet Kaur
10.5120/ijca2016911548

Kimmy Mehta, Navpreet Kaur . An Enhanced Segmentation Technique for Blood Vessel in Retinal Images. International Journal of Computer Applications. 150, 6 ( Sep 2016), 9-15. DOI=10.5120/ijca2016911548

@article{ 10.5120/ijca2016911548,
author = { Kimmy Mehta, Navpreet Kaur },
title = { An Enhanced Segmentation Technique for Blood Vessel in Retinal Images },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 6 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number6/26095-2016911548/ },
doi = { 10.5120/ijca2016911548 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:11.067966+05:30
%A Kimmy Mehta
%A Navpreet Kaur
%T An Enhanced Segmentation Technique for Blood Vessel in Retinal Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 6
%P 9-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the major symptoms of many blood related diseases like diabetes or cardiovascular disease is the change in blood vessel features. These diseases can be detected by analyzing features of retinal vessels and proper treatment can be provided to patient in early stages of disease. Cost associated in detecting these changes and inconsistency in the detection procedure led to the automation of this process. Among other tasks, retinal blood vessel segmentation is the foremost and very challenging task from which various features are analyzed to detect the disease. In this paper, an effective blood vessel segmentation method from coloured retinal fundus images is presented. Segmentation is done by extracting the green channel from RGB retinal image. Firstly the vessel structure is estimated using morphological operations and then noise is removed using Rician Denoise method. After removing the noise, segmentation of blood vessels is carried out using thresholding method. Segmented image needs to be post-processed before considering it for examining any disease. Proposed segmentation method was evaluated on two publicly available DRIVE and STARE datasets. Segmentation process achieves high level of accuracy than most of the previous techniques. Further, results have demonstrated that the proposed method is applicable for segmenting retinal vessels and taking measurements from it. Advantages of this method are its simplicity, fast segmentation process, high efficiency and scalability to deal with coloured retinal images of high resolution.

References
  1. H. R. Taylor and J. E. Keeffe, “World blindness: A 21st century perspective,” Br. J. Ophthalmol., vol. 85, pp. 261–266, 2001.
  2. R. Klein, S. M. Meuer, S. E. Moss, and B. E. Klein, “Retinal microaneurysm counts and 10-year progression of diabetic retinopathy,” Arch. Ophthalmol., vol. 113, pp. 1386–1391, 1995.
  3. P. Massin, A. Erginay, and A. Gaudric, Rétinopathie Diabétique. New York: Elsevier, 2000.
  4. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, pp. 1047–1053, 2004.
  5. S. J. Lee, C. A. McCarty, H. R. Taylor, and J. E. Keeffe, “Costs of mobile screening for diabetic retinopathy: A practical framework for rural populations,” Aust. J. Rural Health, vol. 8, pp. 186–192, 2001.
  6. D. S. Fong, L. Aiello, T. W. Gardner, G. L. King, G. Blankenship, J. D. Cavallerano, F. L. Ferris, and R. Klein, “Diabetic retinopathy,” Diabetes Care, vol. 26, pp. 226–229, 2003.
  7. “Economic costs of diabetes in the U.S. in 2007,” in Diabetes Care. : American Diabetes Association, 2008, vol. 31, pp. 596–615.
  8. American Academy of Ophthalmology Retina Panel, Preferred Practice Pattern Guidelines. Diabetic Retinopathy. San Francisco, CA, Am. Acad. Ophthalmo., 2008 [Online]. Available: http://www.aao.org/ppp
  9. T. Spencer, J. A. Olson, K. C. McHardy, P. F. Sharp, and J.V. Forrester, “An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus,” Comput. Biomed. Res., vol. 29, no. 4, pp. 284–302, 1996.
  10. A. J. Frame, P. E. Undrill, M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, “A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms,” Comput. Biol. Med., vol. 28, no. 3, pp. 225–238, 1998.
  11. M. Larsen, J. Godt, N. Larsen, H. Lund-Andersen, A. K. Sjolie, E. Agardh, H. Kalm, M. Grunkin, and D. R. Owens, “Automated detection of fundus photographic red lesions in diabetic retinopathy,” Investigat. Opht. Vis. Sci., vol. 44, no. 2, pp. 761–766, 2003.
  12. M. Niemeijer, B. van Ginneken, J. J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abramoff, “Automatic detection of red lesions in digital color fundus photographs,” IEEE Trans. Med. Imag., vol. 24, no. 5, pp. 584–592, May 2005.
  13. F. Zana and J. C. Klein, “A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform,” IEEE Trans. Med. Imag., vol. 18, no. 5, pp. 419–428, May 1999.
  14. G. K. Matsopoulos, P. A. Asvestas, N. A. Mouravliansky, and K. K. Delibasis, “Multimodal registration of retinal images using self organizing maps,” IEEE Trans. Med. Imag., vol. 23, no. 12, pp. 1557–1563, Dec. 2004.
  15. C. Heneghan, J. Flynn, M. O’Keefe, and M. Cahill, “Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis,” Med. Image Anal., vol. 6, pp. 407–429, 2002.
  16. E. Grisan and A. Ruggeri, “A divide and impera strategy for the automatic classification of retinal vessels into arteries and veins,” in Proc. 25th Int. Conf. IEEE Eng. Med. Biol. Soc., 2003, pp. 890–893.
  17. Y. Hatanaka, H. Fujita, M. Aoyama, H. Uchida, and T. Yamamoto, “Automated analysis of the distribuitions and geometries of blood vessels on retinal fundus images,” Proc. SPIE Med. Imag. 2004: Image Process., vol. 5370, pp. 1621–1628, 2004.
  18. M. Foracchia, E. Grisan, and A. Ruggeri, “Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images,” in Book Abstracts 2nd Int. Workshop Comput. Asst. Fundus Image Anal., 2001, p. 6.
  19. X. Goa, A. Bharath, A. Stanton, A. Hughes, N. Chapman, and S. Thom, “A method of vessel tracking for vessel diameter measurement on retinal images,” Proc. ICIP, pp. 881–884, 2001.
  20. M. E. Martinez-Perez, A. D. Hughes, A. V. Stanton, S. A. Thom, N. Chapman, A. A. Bharath, and K. H. Parker, “Retinal vascular tree morphology: A semiautomatic quantification,” IEEE Trans. Biomed. Eng., vol. 49, no. 8, pp. 912–917, Aug. 2002.
  21. J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, and R. L. Kennedy, “Measurement of retinal vessel widths from fundus images based on 2-D modeling,” IEEE Trans. Med. Imag., vol. 23, no. 10, pp. 1196–1204, Oct. 2004.
  22. D. E. Becker, A. Can, J. N. Turner, H. L. Tanenbaum, and B. Roysam, “Image processing algorithms for retinal montage, synthesis, mapping and real-time location determination,” IEEE Trans. Biomed. Eng., vol. 45, no. 1, pp. 115–118, Jan. 1998.
  23. H. Shen, B. Roysam, C. V. Stewart, J. N. Turner, and H. L. Tanenbaum, “Optimal scheduling of tracing computations for real-time vascular landmark extraction from retinal fundus images,” IEEE Trans. Inf. Technol. Biomed., vol. 5, pp. 77–91, Mar. 2001.
  24. A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag., vol. 22, no. 8, pp. 951–958, Aug. 2003.
  25. M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trans. Med. Imag., vol. 23, no. 10, pp. 1189–1195, Oct. 2004.
  26. A. A. H. A. R. Youssif, A. Z. Ghalwash, and A. R. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Trans. Med. Imag., vol. 27, no.1, pp. 11–18, Jan. 2008.
  27. H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. Biomed. Eng., vol. 51, no. 2, pp. 246–254, Feb. 2004.
  28. O. Chutatape, L. Zheng, and S. Krishman, “Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters,” in Proc. IEEE Int. Conf. Eng. Biol. Soc., 1998, vol. 20, pp. 3144–3149.
  29. Y. A. Tolias and S. M. Panas, “A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering,” IEEE Trans. Med. Imag., vol. 17, no. 2, pp. 263–273, Apr. 1998.
  30. A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms,” IEEE Trans. Inform. Technol. Biomed., vol. 3, no. 2, pp. 125–138, Jun. 1999.
  31. L. Gagnon, M. Lalonde, M. Beaulieu, and M.-C. Boucher, “Procedure to detect anatomical structures in optical fundus images,” Proc. SPIE Med. Imag.: Image Process., vol. 4322, pp. 1218–1225, 2001.
  32. I. Liu and Y. Sun, “Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme,” IEEE Trans. Med. Imag., vol. 12, no. 2, pp. 334–341, Jun. 1993.
  33. L. Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, “The detection and quantification of retinopathy using digital angiograms,” IEEE Trans. Med. Imag., vol. 13, no. 4, pp. 619–626, Dec. 1994.
  34. 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, ser. Lecture Notes in Computer Science, J. Crespo, V. Maojo, and F. Martin, Eds. Berlin, Germany: Springer-Verlag, 2001, pp. 282–287.
  35. F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process., vol. 10, no. 7, pp. 1010–1019, Jul. 2001.
  36. A. M. Mendonça and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.
  37. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag., vol. 8, no. 3, pp. 263–269, Sep. 1989.
  38. A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag., vol. 19, no. 3, pp. 203–210, Mar 2000.
  39. L. Gang, O. Chutatape, and S. M. Krishnan, “Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter,” IEEE Trans. Biomed. Eng., vol. 49, pp. 168–172, Feb. 2002.
  40. M. Al-Rawi and H. Karajeh, “Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images,” Comput. Methods Programs Biomed., vol. 87, pp. 248–253, 2007.
  41. M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med., vol. 37, pp. 262–267, 2007.
  42. M. G. Cinsdikici and D. Aydin, “Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm,” Comput. Methods Programs Biomed., vol. 96, pp. 85–95, 2009.
  43. 158 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 1, JANUARY 2011
  44. X. Jiang and D. Mojon, “Adaptive local thresholding by verification based multithreshold probing with application to vessel detection in retinal images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 1, pp. 131–137, Jan. 2003.
  45. T. McInerney and D. Terzopoulos, “T-snakes: Topology adaptive snakes,” Med. Imag. Anal., vol. 4, pp. 73–91, 2000.
  46. L. Espona, M. J. Carreira, M. Ortega, and M. G. Penedo, “A snake for retinal vessel segmentation,” Pattern Recognition and Image Analysis, vol. 4478, Lecture Notes Comput. Sci., pp. 178–185, 2007.
  47. 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,” Med. Imag. Anal., vol. 11, pp. 47–61, 2007.
  48. B. S. Y. Lam and H. Yan, “A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields,” IEEE Trans. Med. Imag., vol. 27, no. 2, pp. 237–246, Feb. 2008.
  49. 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,” Br. J. Ophthalmol., vol. 80, pp. 940-944, 1996.
  50. 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,” Br. J. Ophtalmol., vol. 83, pp. 902–910, 1999.
  51. M. Niemeijer, J. Staal, B. v. Ginneken, M. Loog, and M. D. Abramoff, J. Fitzpatrick and M. Sonka, Eds., “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in SPIE Med. Imag., 2004, vol. 5370, pp. 648–656.
  52. J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. V. Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501–509, Apr. 2004.
  53. J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, Jr., H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1214–1222, Sep. 2006.
  54. E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1357–1365, Oct. 2007.
  55. Research Section, Digital Retinal Image for Vessel Extraction (DRIVE) Database. Utrecht, The Netherlands, Univ. Med. Center Utrecht, Image Sci. Inst. [Online]. Available: http://www.isi.uu.nl/Re-search/Databases/DRIVE
  56. STARE ProjectWebsite. Clemson, SC, Clemson Univ. [Online]. Available: http://www.ces.clemson.edu/
  57. Joao V. B. Soares, Jorge J. G. Leandro, Roberto M. Cesar Jr., Herbert F. Jelinek, and Michael J. Cree, “Retinal Vessel Segmentation Using the 2- D Gabor Wavelet and Supervised Classification”, IEEE Trans. Med. Imag., 2010.
  58. Dalwinder Singh, Dharamveer, Birmohan Singh, “New Morphology based Approach for Blood Vessel Segmentation in Retinal Images,”
  59. Rosenfeld, A., and Pfaltz, J. L. (1996) ‘Sequential operations in digital picture processing’, Journal of the ACM, Vol. 13 No. 4, pp. 471–494.
  60. Phansalkar, Neerad, Sumit More, Ashish Sabale, and Madhuri Joshi. "Adaptive local thresholding for detection of nuclei in diversity stained cytology images." In Communications and Signal Processing (ICCSP), 2011 International Conference on, pp. 218-220. IEEE, 2011.
  61. P. Getreuer, M. Tong, and L. A. Vese. A variational model for the restoration of mr images corrupted by blur and rician noise. In Proc. ISVC, pages 686–698, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Blood Vessel Segmentation Diabetic Retinopathy Medical Retinal Imaging.

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