Automatic Blood Vessel Segmentation Based on Adaptive Thresholding Method

Authors

  • A. Pushpalakshmi  Department of Computer Science and Engineering, Sardar Raja College of Engineering, Tamil Nadu, India
  • S. Antony Mutharasan  Department of Computer Science and Engineering, Sardar Raja College of Engineering, Tamil Nadu, India

Keywords:

fundus, tophat morphological reconstruction, stopping criterion, vessel segmentation

Abstract

This paper presents a novel unsupervised method of blood vessel segmentation by iterative algorithm using fundus photographs. There are three stages for segment the blood vessels. In first stage, the negative green plane image is preprocessed to extract the vessel enhanced image. Initial estimate of the segmentation is performed by using global thresholding. Tophat morphological reconstruction is used for extract the vessel enhanced image. In second stage, new pixels are added to the existing vessel estimate iteratively by using adaptive thresholding and the residual image is extracted by removing false edge pixels. A stopping criterion is used to terminate the iterations. In third stage, final estimated vasculature is identified at high accuracy and low computational complexity.

References

  1. Karperien et al., “Automated detection of proliferative retinopathy in clinical practice,” Clin. Ophthalmol. (Auckland, NZ), vol. 2, no. 1, p. 109–122, 2008.
  2. M. Wilson et al., “Computerized analysis of retinal vessel width and tortuosity in premature infants,” Investigative Ophthalmol. Vis. Sci., vol. 49, no. 8, pp. 3577–3585, 2008.
  3. J. Soares et al., “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1214–1222, Sep. 2006.
  4. Marin et al., “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imag., vol. 30, no. 1, pp. 146–158, Jan. 2011.
  5. M. Niemeijer et al., “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. Med. Imag. SPIE,vol. 5370, pp. 648–656, 2004.
  6. 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.
  7. M. Fraz et al., “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Trans. Biomed. Eng., vol. 59, no. 9, pp. 2538–2548, Jun. 2012.
  8. C. Lupascu et al., “Fabc: Retinal vessel segmentation using adaboost,” IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 5, pp. 1267–1274, Sep. 2010.
  9. A. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines andmorphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Aug. 2006.
  10. 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.
  11. M. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng., vol. 58, no. 5, pp. 1183–1192, May 2011.
  12. U. T. V. Nguyen et al., “An effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recogn., vol. 46, no. 3,pp. 703–715, Mar. 2013.
  13. Hoover et al., “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag., vol. 19, pp. 203–210, Mar. 2000.
  14. Budai et al., “Multiscale blood vessel segmentation in retinal fundus images,” in Proc. Bildverarbeitung fr die Med., Mar. 2010, pp. 261–265.
  15. M. Palomera-Perez et al., “Parallel multiscale feature extraction and region growing: Application in retinal blood vessel detection,” IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 2, pp. 500–506, Mar. 2010.
  16. K. A. Vermeer, et al., “A model based method for retinal blood vessel detection,” Comput. Biol. Med., vol. 34, no. 3, pp. 209–219, 2004.
  17. B. 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.
  18. B. Lam et al., “General retinal vessel segmentation using regularizationbased multiconcavity modeling,” IEEE Trans. Med. Imag., vol. 29, no. 7, pp. 1369–1381, Mar. 2010.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2017-12-31

Issue

Volume 2, Issue 2, March-April-2016

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
A. Pushpalakshmi, S. Antony Mutharasan, " Automatic Blood Vessel Segmentation Based on Adaptive Thresholding Method, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.138-141, March-April-2016.