AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES

Md. Alauddin Bhuiyan, Baikunth Nath, Joselito Chua

2007

Abstract

Blood vessel segmentation from the retinal images is extremely important for assessing retinal abnormalities. A good amount of research has been reported on blood vessel segmentation, but significant improvement is still a necessity particularly on minor vessel segmentation. As the local contrast of blood vessels is unstable (intensity variation), especially in unhealthy retinal images, it becomes very complicated to detect the vessels from the retinal images. In this paper, we propose an edge based vessel segmentation technique to overcome the problem of large intensity variation between major and minor vessels. The edge is detected by considering the adaptive value of gradient employing Region Growing Algorithm, from where parallel edges are computed to select vessels. Our proposed method is efficient and performs well in detecting blood vessels including minor vessels.

References

  1. Chanwimaluang, T. and G. Fan (2003). "An efficient blood vessel detection algorithm for retinal images using local entropy thresholding." Proceedings of the 2003 International Symposium on Circuits and Systems (ISCAS 7803) 5: 21-24.
  2. Chaudhuri, S., S. Chatterjee, N. Katz, M. Nelson and M. Goldbaum (1989). "Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filter." IEEE Transactions on Medical Imaging 8(3): 263-269.
  3. Gonzalez, R. C. and P. Wintz (1987). Digital Image Processing, Second Edition. Addison-Wesley Publishing Company, Inc.
  4. Gonzalez, R. C., R. E. Woods, S. L. Eddins (2004). Digital Image Processing Using MATLAB, Prentice Hall.
  5. Hoover, A. (2002). STARE-project http://www.ces.clemson.edu/ahoover/stare (last accessed on 21November, 2006).
  6. Hoover, A., V. Kouznetsova and M. Goldbaum (2000). "Locating Blood Vessels in retinal Images by Piecewise Threshold Probing of a Matched Filter Response." IEEE Transactions on Medical Imaging 19(3): 203-210.
  7. Jiang, X. and D. Mojon (2001). "Blood Vessel Detection in retinal Images by Shape-Based Multi-threshold Probing." Lecture Notes in Computer Science 2191: 38-44.
  8. Jiang, X. and D. Mojon (2003). "Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images." IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1): 131-137.
  9. Li, Q., J. You, L. Zhang and D Zhang (2006). "A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis." Proceedings of International Conference of Pattern recognition (ICPR06): 1-4.
  10. Martinez-Perez, M. E., A. D. Hughes, A. V. Stanton, S. A. Thom, A. A. Bharath and K. H. Parker (1999). "Segmentation of retinal blood vessels based on the second directional derivative and region growing." Proceedings of the International Conference on Image Processing (ICIP 99) 2: 173 - 176.
  11. Mendonca, A. M. and A. Campilho (2006). "Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction." IEEE Transactions on Medical Imaging 25(9): 1200 - 1213.
  12. Staal, J., M. D. Abramoff, M. Niemeijer, M. A. Viergever and B. V. Ginneken (2004). "Ridge-Based Vessel Segmentation in Color Images of the Retina." IEEE Transactions on Medical Imaging 23(4): 501-509.
  13. Tolias, Y. A. and S. M. Panas (1998). "A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering." IEEE Transactions on Biomedical Engineering 17(2): 263-273.
  14. Wu, D., M. Zhang and J-C Liu (2006). "On the Adaptive Detetcion of Blood Vessels in retinal Images." IEEE Transactions on Biomedical Engineering 53(2): 341- 343.
  15. Zana, F. and J. C. Klein (1999). "A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform." IEEE Transactions on Biomedical Engineering 18: 419-428.
  16. Zana, F. and J. C. Klein (2001). "Segmentation of vessellike patterns using mathematical morphology and curvature evaluation." IEEE Transactions on Image Processing 10(7): 1010-1019.
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Paper Citation


in Harvard Style

Alauddin Bhuiyan M., Nath B. and Chua J. (2007). AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 404-409. DOI: 10.5220/0002054104040409


in Bibtex Style

@conference{visapp07,
author={Md. Alauddin Bhuiyan and Baikunth Nath and Joselito Chua},
title={AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={404-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002054104040409},
isbn={978-972-8865-73-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES
SN - 978-972-8865-73-3
AU - Alauddin Bhuiyan M.
AU - Nath B.
AU - Chua J.
PY - 2007
SP - 404
EP - 409
DO - 10.5220/0002054104040409