Abstract
In this work, diverse condition of craftsmanship strategies for retinal vein division was executed and investigated. Right off the bat, an administered strategy in light of dark level and minute invariant highlights with neural system was investigated. Alternate counts considered were an unsupervised strategy in view of dark level co-event framework with nearby entropy and a coordinated separating technique in light of first request subordinate of Gaussian. Amid the work, openly accessible picture database DRIVE was used for assessing the execution of the calculations which incorporates affectability, specificity, exactness, positive prescient esteem and negative prescient esteem. The accuracy for the blood vessel segmentation was very near to which was given in the literature which was referred. Now because the sensitivity of all the methods used by us was lower, it leads to lower number of correctly classed vessels from the images taken. The results achieved have tremendous potential for application in real life, and in practical use, only a little bit of modification is required to get better segmentation between vessels and the corresponding background.
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References
Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25, 1214–1222 (2006)
Fathi, A., Naghsh-Nilchi, A.R.: Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed. Signal Process. Control 8(1), 71–80 (2012)
Fang, B., Hsu, W., Lee, M.U.: On the Detection of Retinal Vessels in Fundus Images. http://hdl.handle.net/1721.1/3675. (04.05.2016)
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26, 1357–1365 (2007)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)
Sinthanayothin, C., Boyce, J., Williamson, C.T.: Automated localisation of the optic disk, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999)
Villalobos-Castaldi, F.M., Felipe-Riveron, E.M., Sanchez-Fernandez, L.P.: A fast, efficient and automated method to extract vessels from fundus images. J. Vis. 13, 263–270 (2010)
Kande, G.B., Subbaiah, P.V., Savithri, T.S.: Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. J. Med. Syst. 34, 849–858 (2009)
Rahebi, J., Hardalac, F.: Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J. Med. Syst. 38(8), 85–97 (2014)
Tolias, Y., Panas, S.: A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging 17(2), 263–273 (1998)
Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 37, 262–267 (2006)
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8, 263–269 (1989)
Zolfagharnasab, H., Naghsh-Nilchi, A.R.: Cauchy based matched filter for retinal vessels detection. J. Med. Signals Sens. 4(1), 1–9 (2014)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Pattona, N., Aslam, T.M., MacGillivray, T., Deary, I.J.: Retinal Image Analysis: Concepts, Applications and Potential (2006)
Staal, J.J., Abrmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)
Chauduri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)
You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn. 44, 2314–2324 (2011)
Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46, 703–715 (2013)
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Singh, K., Raviteja, K., Puntambekar, V., Mahalakshmi, P. (2020). Design and Implementation of a Blood Vessel Identification Algorithm in the Diagnosis of Retinography. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_52
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