Advertisement

Design and Implementation of a Blood Vessel Identification Algorithm in the Diagnosis of Retinography

  • Kumarkeshav Singh
  • Kalugotla Raviteja
  • Viraj Puntambekar
  • P. MahalakshmiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)

Abstract

In this work, diverse condition of craftsmanship strategies for retinal vein division was executed and investigated. Right off the bat, an administered strategy in light of dark level and minute invariant highlights with neural system was investigated. Alternate counts considered were an unsupervised strategy in view of dark level co-event framework with nearby entropy and a coordinated separating technique in light of first request subordinate of Gaussian. Amid the work, openly accessible picture database DRIVE was used for assessing the execution of the calculations which incorporates affectability, specificity, exactness, positive prescient esteem and negative prescient esteem. The accuracy for the blood vessel segmentation was very near to which was given in the literature which was referred. Now because the sensitivity of all the methods used by us was lower, it leads to lower number of correctly classed vessels from the images taken. The results achieved have tremendous potential for application in real life, and in practical use, only a little bit of modification is required to get better segmentation between vessels and the corresponding background.

Keywords

Matched filtering Performance measure Retinal image Retinal vessel segmentation Supervised method Unsupervised method 

References

  1. 1.
    Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25, 1214–1222 (2006)CrossRefGoogle Scholar
  2. 2.
    Fathi, A., Naghsh-Nilchi, A.R.: Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed. Signal Process. Control 8(1), 71–80 (2012)CrossRefGoogle Scholar
  3. 3.
    Fang, B., Hsu, W., Lee, M.U.: On the Detection of Retinal Vessels in Fundus Images. http://hdl.handle.net/1721.1/3675. (04.05.2016)
  4. 4.
    Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26, 1357–1365 (2007)CrossRefGoogle Scholar
  5. 5.
    Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)CrossRefGoogle Scholar
  6. 6.
    Sinthanayothin, C., Boyce, J., Williamson, C.T.: Automated localisation of the optic disk, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999)CrossRefGoogle Scholar
  7. 7.
    Villalobos-Castaldi, F.M., Felipe-Riveron, E.M., Sanchez-Fernandez, L.P.: A fast, efficient and automated method to extract vessels from fundus images. J. Vis. 13, 263–270 (2010)CrossRefGoogle Scholar
  8. 8.
    Kande, G.B., Subbaiah, P.V., Savithri, T.S.: Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. J. Med. Syst. 34, 849–858 (2009)CrossRefGoogle Scholar
  9. 9.
    Rahebi, J., Hardalac, F.: Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J. Med. Syst. 38(8), 85–97 (2014)CrossRefGoogle Scholar
  10. 10.
    Tolias, Y., Panas, S.: A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging 17(2), 263–273 (1998)CrossRefGoogle Scholar
  11. 11.
    Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 37, 262–267 (2006)CrossRefGoogle Scholar
  12. 12.
    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. Imaging 8, 263–269 (1989)CrossRefGoogle Scholar
  13. 13.
    Zolfagharnasab, H., Naghsh-Nilchi, A.R.: Cauchy based matched filter for retinal vessels detection. J. Med. Signals Sens. 4(1), 1–9 (2014)CrossRefGoogle Scholar
  14. 14.
    Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)CrossRefGoogle Scholar
  15. 15.
    Pattona, N., Aslam, T.M., MacGillivray, T., Deary, I.J.: Retinal Image Analysis: Concepts, Applications and Potential (2006)CrossRefGoogle Scholar
  16. 16.
    Staal, J.J., Abrmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  17. 17.
    Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)CrossRefGoogle Scholar
  18. 18.
    Chauduri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)CrossRefGoogle Scholar
  19. 19.
    You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn. 44, 2314–2324 (2011)CrossRefGoogle Scholar
  20. 20.
    Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46, 703–715 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Kumarkeshav Singh
    • 1
  • Kalugotla Raviteja
    • 1
  • Viraj Puntambekar
    • 1
  • P. Mahalakshmi
    • 1
    Email author
  1. 1.School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations