Call for Paper - May 2020 Edition
IJCA solicits original research papers for the May 2020 Edition. Last date of manuscript submission is April 20, 2020. Read More

Extraction of the Retinal Blood Vessels and Detection of the Bifurcation Points

Print
PDF
International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 77 - Number 2
Year of Publication: 2013
Authors:
Manjiri B. Patwari
Ramesh R. Manza
Yogesh M. Rajput
Neha K. Deshpande
Manoj Saswade
10.5120/13367-0967

Manjiri B Patwari, Ramesh R Manza, Yogesh M Rajput, Neha K Deshpande and Manoj Saswade. Article: Extraction of the Retinal Blood Vessels and Detection of the Bifurcation Points. International Journal of Computer Applications 77(2):29-34, September 2013. Full text available. BibTeX

@article{key:article,
	author = {Manjiri B. Patwari and Ramesh R. Manza and Yogesh M. Rajput and Neha K. Deshpande and Manoj Saswade},
	title = {Article: Extraction of the Retinal Blood Vessels and Detection of the Bifurcation Points},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {77},
	number = {2},
	pages = {29-34},
	month = {September},
	note = {Full text available}
}

Abstract

The changes in retinal blood vessels Structure and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. Proposed algorithm for the detection and measurement of blood vessels of the retina and finding the bifurcation points of blood vessels is general enough that it can be applied to high resolution fundus photographs. The algorithm proceeds through three main steps 1. Preprocessing operations on high resolution fundus images 2. For retinal vessel extraction, simple vessel segmentation techniques formulated in the language of 2D Median Filter 3. Minutiae techniques for finding bifurcation points of the extracted blood vessels. Performance of this algorithm is tested using the fundus image database ( 240 images) taken from Dr. Manoj Saswade, Dr. Neha Deshpande and online available databases diaretdb0, diaretdb1 and DRIVE. This algorithm achieves accuracy of 96% with 0. 92 sensitivity and 0 specificity for Saswade database , for diaretdb0 accuracy 95% with 0. 95 sensitivity and 0 specificity, for diaretdb1 accuracy 96% with 0. 96 sensitivity and 0 specificity, and for DRIVE database 98% accuracy with 0. 98 sensitivity and 0 specificity.

References

  • Knudtson MD, Klein BEK, Klein R, Wong TY, Hubbard LD, et al. Variation associated with measurement of retinal vessel diameters at different points in the pulse cycle. Br J Ophthalmol. 2004;88:57–61.
  • Wong LY, U'Rajendra A, Venkatesh YV, Caroline C, Lim CM, Ng EYK: Identification of different stages of Diabetic retinopathy using retinal optical images. Inf Sci 2008, 178:106-121.
  • Rangaraj M. Rangayyan,1 Xiaolu Zhu,1 Fábio J. Ayres,1 and Anna L. Ells2 "Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis" Journal of Digital Imaging, Vol 23, No 4 (August), 2010: pp 438Y453
  • S. Jiméneza,P. Alemanya, I. Fondónb, A. Foncubiertab, B. Achab and C. Serranob "Automatic detection of vessels in color fundus images" © 2009 Sociedad Española de Oftalmología. Published by Elsevier España, s. larchsocespoftalmol. 2010;85(3):103-109
  • Ana MM, Aurelio C: Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines And Morphological Reconstruction. IEEE Trans Medical imaging 2006, 25:1200-1213.
  • Fischer JG, Mewes H, Hopp HH, Schubert R. Analysis of pressurized resistance vessel diameter changes with a low cost digital image processing device. Comput Meth Prog Bio. 1996;50:23–30.
  • Patton N, Aslam T, Macgillivray T, Pattie A, Deary IJ, et al. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. 2005;206:319–348.
  • R. Winder, P. Morrow, I. McRitchie, J. Bailie, and P. Hart, "Algorithms for digital image processing in diabetic retinopathy," Computerized Med. Imag. Graph. , vol. 33, pp. 608–622, 2009.
  • H. Li, W. Hsu, M. L. Lee, and H. Wang, "A piecewise gaussian model for profiling and differentiating retinal vessels," International Conference on Image Processing, 2003.
  • Manjiri B. Patwari, Dr. Ramesh R. Manza, Dr. Manoj Saswade and Dr. Neha Deshpande, "A Critical Review of Expert Systems for Detection and Diagnosis of Diabetic Retinopathy", Ciit International Journal of Fuzzy Systems, February 2012, DOI: FS022012001 ISSN 0974-9721, 0974-9608. (IF 0. 441).
  • Yogesh M. Rajput, Ramesh R. Manza, Manjiri B. Patwari, Neha Deshpande, "Retinal Blood Vessels Extraction Using 2D Median Filter", Third National Conference on Advances in Computing(NCAC-2013), 5th to 6th March 2013, School of Computer Sciences, North Maharashtra University, Jalgaon-425001 (MS) India.
  • Yogesh M. Rajput, Ramesh R. Manza, Manjiri B. Patwari, Neha Deshpande, "Retinal Optic Disc Detection Using Speed Up Robust Features", National Conference on Computer & Management Science [CMS-13], April 25-26, 2013, Radhai Mahavidyalaya, Auarngabad-431003(MS India).
  • A. Hoover, V. Kouznetsova and M. Goldbaum, Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000.
  • A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels", IEEE Transactions on Medical Imaging , vol. 22 no. 8, pp. 951-958, August 2003.
  • A. Hoover and M. GoldBaum. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. on Medical Imaging
  • M. Sofka and C. V. Stewart. Retinal vessel extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. on Medical Imaging, 25(12):1531–1546, 2006. 2
  • J. Staal,M. D. Abramoff,M. Niemeijer,M. A. Viergever, and B. V. Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. on Medical Imaging, 23(4):501– 509, 2004.
  • J. L. Company, "Grading diabetic retinopathy from stereoscopic color fundus photographs - an extension of the modified airlie house classification," ETDRS Report No. 10, Ophthalmology, the Journal of the American Academy of Ophthalmology, vol. 98, no. 5, p. 78, May 1991.
  • O. Chutatape, L. Zheng, and S. M. Krishnan, "Retinal blood vessel detection and tracking by matched gaussian and kalman filters," Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology society, vol. 20, no. 6, pp. 3144– 3149, 1998.
  • H. Li, W. Hsu, M. L. Lee, and H. Wang, "A piecewise gaussian model for profiling and differentiating retinal vessels," International Conference on Image Processing, 2003.
  • M. Turner, "Texture discrimination by gabor functions," Biological Cybernetics, vol. 55, pp. 71–82, 1986.
  • A. Bovik, M. Clark, and W. Geisler, "Multichannel texture analysis using localized spatial filters," IEEE Trans. PAMI, vol. 12, no. 1, pp. 55–73, 1990.
  • A. Jain and F. Farrokhnia, "Unsupervised texture segmentation using gabor filters," Intl. Journal of Computer Vision, pp. 279–302, 1988.