Journal of Digital Imaging

, Volume 24, Issue 4, pp 564–572 | Cite as

An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection



This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Key Words

Diabetic retinopathy blood vessel segmentation automatic thresholding histogram equalization 


  1. 1.
    Kanski JJ: Clinical Ophthalmology: A Systematic Approach. Butterworth-Heinemann, London, UK, 1989Google Scholar
  2. 2.
    Sussman EJ, Tsiaras WG, Soper KA: Diagnosis of diabetic eye disease. J Am Med Assoc 247:3231–3234, 1982CrossRefGoogle Scholar
  3. 3.
    Lee SJ, McCarty CA, Taylor HR, Keeffe JE: Costs of mobile screening for diabetic retinopathy: A practical framework for rural populations. Aust J Rural Health 8:186–192, 2001CrossRefGoogle Scholar
  4. 4.
    Taylor HR, Keeffe JE: World blindness: A 21st century perspective. Brit J Ophthalmol 85:261–266, 2001CrossRefGoogle Scholar
  5. 5.
    Streeter L, Cree MJ: Microaneurysm detection in colour fundus images. In: Image and Vision Computing. New Zealand, Palmerston North, New Zealand, Nov. 2003, pp. 280–284Google Scholar
  6. 6.
    Soares JVB, Leandro JJG, Cesar Jr, RM, Jelinek HF, Cree MJ: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imag 25(9):1214–1222, 2006CrossRefGoogle Scholar
  7. 7.
    Hoover A, Kouznetsova V, Goldbaum M: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imag 19(3):203–210, 2000CrossRefGoogle Scholar
  8. 8.
    Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imag 8:263–269, 1989CrossRefGoogle Scholar
  9. 9.
    Tolias Y, Panas S: A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imag 17:263–273, 1998CrossRefGoogle Scholar
  10. 10.
    Sun Y: Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans Med Imag 8:78–88, 1989CrossRefGoogle Scholar
  11. 11.
    Tamura S, Okamoto Y, Yanashima K: Zero-crossing interval correction in tracing eye-fundus blood vessels. Pattern Recognit 21(3):227–233, 1988CrossRefGoogle Scholar
  12. 12.
    Tamura S, Tanaka K, Ohmori S, Okazaki K, Okada A, Hoshi M: Semiautomatic leakage analyzing system for time series fluorescein ocular fundus angiography. Pattern Recognit 16(2):149–162, 1983CrossRefGoogle Scholar
  13. 13.
    Jiang X, Mojon D: Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25(1):131–137, 2003CrossRefGoogle Scholar
  14. 14.
    Thackray BD, Nelson AC: Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Trans Med Imag 12:385–392, 1993CrossRefGoogle Scholar
  15. 15.
    Klein AK, Lee F, Amini A: Quantitive coronary angiography with deformable spline models. IEEE Trans Med Imag 16:468–482, 1997CrossRefGoogle Scholar
  16. 16.
    Nekovei R, Sun Y: Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans Neural Networks 6:64–72, 1995CrossRefGoogle Scholar
  17. 17.
    Thackray BD, Nelson AC: Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Trans Med Imag 12:385–392, 1993CrossRefGoogle Scholar
  18. 18.
    Ritchings RT, Colchester ACF: Detection of abnormalities on carotid angiograms. Pattern Recogn Lett 4:367–374, 1986CrossRefGoogle Scholar
  19. 19.
    Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE: Contrast-limited adaptive histogram equalization: Speed and effectiveness. In: Proc. of the 1st Conf. on Visualization in Biomedical Computing, 1990, pp 337–345Google Scholar
  20. 20.
    Ridler TW, Calvard: Picture thresholding using an iterative selection method. In: Proc. IEEE Trans. On Systems, Man, Cybernetics, vol. SMC-8, 1978, pp 630–632Google Scholar
  21. 21.
    Niemeijer M, van Ginneken B: 2002 [Online]. Available:
  22. 22.
    Leandro JJG, Soares JVB, Cesar RM Jr., Jelinek HF: Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers. In: Proc. of the 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), 2003, pp 262–269Google Scholar
  23. 23.
    Salem NM, Nandi AK: Novel and adaptive contribution of the red channel in pre-processing of colour fundus images. J Franklin Inst 344:243–256, 2007CrossRefGoogle Scholar
  24. 24.
    Jain AK: Fundamental of digital image processing. Prentice Hall, 1989, ISBN: 0133325764Google Scholar
  25. 25.
    Hossain F, Alsharif MR: Image enhancement based on logarithmic transform coefficient and adaptive histogram equalization. In: Proc. of Int. Conf. on Convergence Information Technology. 2007, pp 1439–1444Google Scholar
  26. 26.
    The MathWorks, Inc, (1994–2010). The Matlab package, [online]. Available: [2008, October 18]
  27. 27.
    Papadopoulos A, Fotiadis DI, Costaridou L: Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. J Comput Biol Med 38:1045–1055, 2008CrossRefGoogle Scholar
  28. 28.
    Costa LF, Cesar RM Jr: Shape analysis and classification: Theory and practice. Boca Raton: CRC Press, 2001, ISBN 0-8493-3493-4Google Scholar
  29. 29.
    Kwan HK: Fuzzy filters for noisy image filtering. In: Proc. Int. Sym. on Circuits and Systems (ISCAS), vol. 4, 2003, pp 161–164Google Scholar
  30. 30.
    Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Englewood Cliffs, NJ: Prentice hall, 2002. ISBN: 0201180758Google Scholar
  31. 31.
    Mendonca AM: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. In: Proc. IEEE Trans. on Med. Imag., vol. 25, no. 9, pp. 1200–1213, 2006Google Scholar
  32. 32.
    Metz CE: Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298, 1978PubMedCrossRefGoogle Scholar
  33. 33.
    Cohen J: A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46, 1960CrossRefGoogle Scholar
  34. 34.
    Staal J, Abramoff MD, Niemeijer M, Viergever MA, Van Ginneken B: Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Img 23(4):501–509, 2004CrossRefGoogle Scholar
  35. 35.
    Niemeijer M, Staal J, Van Ginneken B, Loog M, Abramoff MD: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick M, Sonka M Eds. Proc. SPIE Med. Image, vol. 5370, 2004, pp. 648–656Google Scholar
  36. 36.
    Reza AW, Eswaran C, Hati S: Diabetic retinopathy: A quadtree based blood vessel detection algorithm using RGB components in fundus images. J Med Syst 32(2):147–155, 2008PubMedCrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  1. 1.Centre for Communication Infrastructure, Faculty of Information TechnologyMultimedia UniversityCyberjayaMalaysia

Personalised recommendations