CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Extraction of Blood Vessels from Retinal Image using Adaptive Morphology

by Rajat Suvra Nandy, Rohit Kamal Chatterjee, Avijit Kar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 11
Year of Publication: 2017
Authors: Rajat Suvra Nandy, Rohit Kamal Chatterjee, Avijit Kar
10.5120/ijca2017914543

Rajat Suvra Nandy, Rohit Kamal Chatterjee, Avijit Kar . Extraction of Blood Vessels from Retinal Image using Adaptive Morphology. International Journal of Computer Applications. 168, 11 ( Jun 2017), 28-34. DOI=10.5120/ijca2017914543

@article{ 10.5120/ijca2017914543,
author = { Rajat Suvra Nandy, Rohit Kamal Chatterjee, Avijit Kar },
title = { Extraction of Blood Vessels from Retinal Image using Adaptive Morphology },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 11 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number11/27921-2017914543/ },
doi = { 10.5120/ijca2017914543 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:53.463702+05:30
%A Rajat Suvra Nandy
%A Rohit Kamal Chatterjee
%A Avijit Kar
%T Extraction of Blood Vessels from Retinal Image using Adaptive Morphology
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 11
%P 28-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a new algorithm is proposed for segmentation of the retinal blood vessels in the ophthalmoscopic images using Mathematical Morphology based technique named as Adaptive Line Structuring Element (ALSE). The ALSE is rotated to adjust with the curvature of the blood vessels which ensure that each part of the vessel components remains connected and followed by changing the size of the line structuring element that adaptively discards non-vessel like objects in a fundus image. This forms a Scale-Space that extracts the blood vessels structure in retina successfully from finer scales to coarser scale without much loss of data. For the purpose of evaluating performance of proposed algorithm two well-known criterion function namely Structural Similarity (SSIM) index and Figure of Merit (FOM) index are used for comparing the results. The average value of SSIM and FOM indicates better performance of the proposed algorithm than other recent methods.

References
  1. Li, Q., You, J., Zhang, L. and Bhattacharya, P. 2006. Automated Retinal Vessel Segmentation Using Multiscale Analysis and Adaptive Thresholding. IEEE Trans. Image Analysis and Interpretation, pp.139-143.
  2. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. and Goldbaum, M. 1989. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag., vol. 8, pp. 263–269.
  3. Pappas, T. and Lim, J. 1988. A new method for estimation of coronary artery dimensions in angiograms. IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 1501–1513.
  4. Sun, Y. 1989. Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans. Med. Imag., vol. 8, pp. 78–88.
  5. Tamura, S., Okamoto, Y. and Yanashima, K. 1988. Zero crossing interval correction in tracing eye-fundus blood vessels. Pattern Recognition., vol. 21, no. 3, pp. 227-233.
  6. Tolias, Y. and Panas, S. 1998. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imag., vol. 17, pp. 263–273.
  7. Hoover, A. D., Kouznetsova, V. and Goldbaum, M. 2000. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. On Medical Imaging, vol. 19, no. 3, pp. 203–210.
  8. Gonzalez, R.C. Woods, R. E. 2008. Digital Image Processing. Pearson Education. Third Edition, pp. 627-687.
  9. Joshi, S. and Karule, P. T. 2012. Retinal Blood Vessel Segmentation: International Journal of Engineering and Innovative Technology (IJEIT) Vol. 1, Issue 3.
  10. Salem, N. M. and Nandi, A. K. 2008. Unsupervised Segmentation of Retinal Blood Vessels using a Single Parameter Vesselness Measure. Sixth Indian Conference on Computer Vision, Graphics & Image Processing, IEEE Xplore, pp. 528-534.
  11. Staal, J. J., Abramoff, M. D., Niemeijer, M., Viergever, M. A. and Ginneken, B. V. 2004. Ridge-Based Vessel Segmentation in Colour Images of the Retina. IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501– 509.
  12. Jiang, X. and Mojon, D. 2003. Adaptive Local Thresholding by Verification Based Multi-threshold Probing with Application to Vessel Detection in Retinal Images. IEEE Trans. Pattern Anal. Machine Intell., vol. 25, pp. 131–137.
  13. Zana, F. and Klein, J. C. 2001. Segmentation of Vessel-like Patterns Using Mathematical Morphology and Curvature Evaluation. IEEE Trans. on Medical Imaging, vol. 10, no. 7, pp. 1010–1019.
  14. Samanta, S., Saha, S. K. and Chanda, B. 2011. A Simple and Fast Algorithm to Detect the Fovea Region in Fundus Retinal Image. Second International Conference on Emerging Applications of Information Technology, IEEE Xplore, pp. 206 – 209.
  15. Wang, Z., Bovik, A. C., Sheikh, H.R. and Simoncelli, E. P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , vol. 13 Issue 4, pp. 600–612 ( April 2004)
  16. DRIVE: Digital Retinal Images for Vessel Extraction http://www.isi.uu.nl/Research/Databases/DRIVE/ as on June 30 (2012).
  17. Abdou, I. E. and Pratt, W. K. 1979. Quantitative design and evaluation of enhancement/thresholding edge detectors. In Proceedings of the IEEE Conference. 67(5). 753-763.
  18. Kundu, A. and Chatterjee, R. K. 2012. Retinal vessel segmentation using Morphological Angular Scale-Space. 2012 Third International Conference on Emerging Applications of Information Technology.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Mathematical Morphology Adaptive Line Structuring Element Retinal Images Curvature Scale-Space.

Powered by PhDFocusTM