Abstract
Extraction of Retinal blood vessels are important for computer-aided diagnosis of ophthalmologic diseases. This paper presents a novel method for the detection of blood vessels of the retina. In proposed algorithm, at first, the background of the retinal vessels is removed. Then we perform modified Gaussian filtering to remove the noise of the image. The resulting image is subjected to Bottom-hat transformation and thereafter a width-dependant threshold operation is carried out to extract the blood vessels effectively. The proposed method is very efficient and less computational time as compared to the previously implemented methods and is thus very useful.
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References
Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Chapman, N., Bharath, A.A., Parker, K.H.: Retinal vascular tree morphology: a semiautomatic quantification. IEEE Trans. Biomed. Eng. 49(8), 912–917 (2002)
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)
Teng, T., Lefley, M., Claremont, D.: Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med. Biol. Eng. Comput. 40, 2–13 (2002)
Jelinek, H.F., Cree, M.J., Leandro, J.J.G., Soares, J.V.B., Cesar, R.M., Luckie, A.: Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy. J. Opt. Soc. Am. A 24, 1448–1456 (2007)
Ponto, K.A., Werner, D.J., Wiedemer, L., Laubert-Reh, D., Schuster, A.K., Nickels, S., Höhn, R., Schulz, A., Binder, H., Beutel, M., Lackner, K.J., Wild, P.S., Pfeiffer, N., Mirshahi, A.: Retinal vessel metrics: normative data and their use in systemic hypertension results from the Gutenberg Health Study. J. Hypertens. 35, 1635–1645 (2017) (Wolters Kluwer Health Inc.)
Imani E., Javidi M., Pourreza H. R.: Improvement of retinal blood vessel detection using morphological component analysis. Comput. Methods Programs Biomed. 118(3), 263–279 (2015). ISSN: 1872-7565
Wan Mustafa W.A.B., 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)
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)
Patil, V.P., Wankhede, P.R.: Pre-processing steps for segmentation of retinal blood vessels. Int. J. Comput. Appl. 94(12), 34–37 (2014)
Halder, A., Sarkar, A., Ghose, S.: Adaptive histogram equalization and opening operation-based blood vessel extraction. In: Soft Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol. 758, pp. 557–564 (2018)
Vijaykumar, V., Vanathi, P., Kanagasabapathy, P.: Fast and efficient algorithm to remove gaussian noise in digital images. IAENG Int. J. Comput. Sci. 37(1), 78–84 (2010)
Ma, J., Fan, X., Yang, S.X., Zhang, X., Zhu, X.: Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement. Preprints (2017)
Staal, J.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(4), 501–509 (2004)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19, 203–210 (2000)
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Halder, A., Ghose, S. (2020). Blood Vessel Extraction from Retinal Images Using Modified Gaussian Filter and Bottom-Hat Transformation. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_30
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DOI: https://doi.org/10.1007/978-981-13-9042-5_30
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