Abstract
Retinal blood vessel detection is a fundamental procedure for automatic detection of retinal diseases and infections. This paper presents a method for blood vessel detection of retinal images using adaptive histogram equalization and morphological operations. After proper intensity adjustments, the image is subjected to morphological operations and then passed through a median filter. Threshold of the filtered image is then carried out to give a resultant image. The final image is obtained using vessel width dependent morphological filters to remove all the connected components that have fewer than the required pixel width; i.e., all the small components are removed. The performance of the proposed is very promising as compared to the existing techniques.
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Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Kennedy, R.L.: Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans. Med. Imag. 23(10), 1196–1204 (2004)
Staal, J., Abramoff, 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, 501–509 (2004)
Wong T.Y., Shankar, A., Klein, R., Klein, B.E.K., Hubbard, L.D.: Prospective cohort study of retinal vessel diameters and risk of hypertension. BMJ, 1–5 (2004)
Udayakumar, R., Khanaa, V., Saravanan, T., Saritha, G.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. Middle-East J. Sci. Res. 12(12), 1668–1671 (2013)
Yang, Y., Huang, S., Rao, N.: An automatic hybrid method for retinal blood vessel extraction. Int. J. Appl. Math. Comput. Sci. 18(3), 399–407 (2008)
Salazar-Gonzalez, A., Kaba, D., Li, Y., Liu, X.: Segmentation of the blood vessels and optic disc in retinal images. IEEE J. Biomed. Health Inform. 2168–2194 (2014)
Niemeijer, M., Staal, J., Ginneken, B., van Loog, M., Abrmoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. Proceedings of the SPIE Medical Imaging 5370, 648–656 (2004)
Fritzsche, K.H., Can, A., Shen, H., Tsai, C.L., Turner, J.N., Tanenbaum, H.L., Stewart, C.V., Roysam, B.: Automated model-based segmentation, tracing and analysis of retinal vasculature from digital fundus images. In: Angiography and Plaque Imaging, pp. 225–297 (2003)
Mustafa W.A.B.W., Yazid, H., Bin Yaacob, S., Bin Basah, S.N.: Blood vessel extraction using morphological operation for diabetic retinopathy. In: 2014 IEEE Region 10 Symposium (2014)
Hassan, G., El-Bendary, N., Hassanienc, A.E., Fahmy, A., Shoeb, A.M., Snasel, V.: Retinal blood vessel segmentation approach based on mathematical morphology. Procedia Comput. Sci. 625, 612–622 (2015)
De, I., Das, S., Ghosh, D.: Vessel extraction in retinal images using morphological filters. In: International Conference on Research in Computational Intelligence and Communication Networks (2015)
Walter, T., Klein, J.C.: Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. Lecture Notes Computer Science 2199, 282–287 (2001)
Wankhede, P.R., Khanchandani, K.B.: Noise removal and background extraction from retinal fundus images for segmentation of blood vessels. Int. J. Graph. Image Process. 3(1) (2013)
Halder, A., Bhattacharya, P.: An application of bottom hat transformation to extract blood vessel from retinal images. In: 2015 International Conference on Communications and Signal Processing, pp. 1791–1795 (2015)
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Halder, A., Sarkar, A., Ghose, S. (2019). Adaptive Histogram Equalization and Opening Operation-Based Blood Vessel Extraction. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_54
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DOI: https://doi.org/10.1007/978-981-13-0514-6_54
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