Abstract
Segmentation from colored retina images plays a vital role in stable feature extraction for image registration and detection in many ocular diseases. In this study, the authors will look at the segmentation of the blood vessels from fundus images which will further help in preparation of digital template. Here, images are passed through the preprocessing stages and then some of the morphological operators for thresholding are applied on the images for segmentation. Finally, noise removal and binary conversion complete the segmentation method. Then, a number count on blood vessels around the optic disk is done as a feature for further processing. The authors will ensure whether the segmentation accuracy, based on comparison with a ground truth, can serve as a reliable platform for image registration and ocular disease detection. Experiments are done on the images of DRIVE and VARIA databases with an average accuracy of 97.20 and 96.45%, respectively, for segmentation, and a comparative study has also been shown with the existing works.
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References
J.L. Boulnois, Photo physical processes in recent medical laser developments. Lasers Med. Sci. 1, 47–66 (1986)
G.M. Bohigian, Lasers in medicine and surgery. JA-MA 256, 900–907 (1986)
M.D. Abrmoff, M. Niemeijer, The automatic detection of the optic disc location in retinal images using optic disc location regression, in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS ’06 (2006)
The DRIVE database, Image sciences institute, university medical center utrecht, The Netherlands. http://www.isi.uu.nl/Research/Databases/DRIVE/. Last accessed 7 July 2007
VARIA Database, Department of Computer Science of the Faculty of Informatics of the University of Corua. http://www.varpa.es/varia.html
Y.A. Tolias, S.M. Panas, A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging 17, 263–273 (1998)
B. Dizdarog, E. Ataer-Cansizoglu, J. Kalpathy-Cramer, K. Keck, M.F. Chiang, D. Erdogmus, Structure-based level set method for automatic retinal vasculature segmentation. EURASIP J. Image Video Process. (2014). (Springer)
P.C. Siddalingaswamy, K.G. Prabhu, Automatic segmentation of blood vessels in colour retinal images using spatial gabor filter and multiscale analysis, in 13th International Conference on Biomedical Engineering, IFMBE Proceedings, vol. 23 (Springer, 2009), pp. 274–276
D. Wu, M. Zhang, J.C. Liu, W. Bauman, On the adaptive detection of blood vessels in retinal images. IEEE Trans. Biomed. Eng. 53(2), 341–343 (2006)
M.M. Fraz, P. Remagnino, A. Hoppe, S. Velastin, B. Uyyanonvara, S.A. Barman, A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features, in IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (2011), pp. 16–18
A. Bhuiyan, B. Nath, J. Chua, R. Kotagiri, Blood vessel segmentation from color retinal images using unsupervised texture classification, in IEEE International Conference, ICIP 2007, vol. 5 (2007)
A.M. Mendona, A. Campilho, Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25, 1200–1213 (2006)
X. You, Q. Peng, Y. Yuan, Y. Cheung, J. Lei, Segmentation of Retinal Blood Vessels Using the Radial Projection and Semi-supervised Approach (Elsevier Science Inc., New York, NY, USA, 2011), pp. 2314–2324
D. Marin, A. Aquino, M.E. Gegundez-Arias, J.M. Bravo, A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2010)
F.M. Villalobos-Castaldi, E.M. Felipe-Rivern, L.P. Snchez-Fernndez, A fast, efficient and automated method to extract vessels from fundus images. J. Visual. 13(3), 263–270 (2010). (Springer)
D.S. Fong, L. Aiello, T.W. Gardner, G.L. King, G. Blankenship, J.D. Cavallerano, F.L. Ferris, R. Klein, Diabetic retinopathy. Diabetes Care 26, 226229 (2003)
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Goswami, S., Goswami, S., Roy, S., Mukherjee, S., Roy, N.D. (2020). An Automated Segmentation Approach from Colored Retinal Images for Feature Extraction. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_2
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