Abstract
In retinal images, vessel segmentation methods are an important component of circulatory blood vessel analysis systems. This paper introduces an effective approach to segment the vessels in the fundus images. The fundus images are first enhanced using curvelet transform, then segmentation is performed using morphological operations with a modified structuring element and length filtering. The proposed method has been tested on 40 images of the DRIVE database. The results demonstrate that the proposed algorithm segments blood vessels in the retinal images effectively with an accuracy of 94.33%.
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References
Youssif, A., Ghalwash, A., Ghoneim, A.: Optic Disc Detection from Normalized Digital Fundus Images by Means of a Vessels’ Direction Matched Filter. IEEE Trans. on Medical Imaging 27(1) (2008)
Michal, S., Charles, V.S.: Retinal Vessel Extraction using Multiscale Matched Filters, Confidence and Edge Measures. IEEE Trans. on Medical Imaging 25(12), 1531–1546 (2006)
Candès, E., Demanet, L.: Curvelets and Fourier Integral Operators. C. R. Math. Acad. Sci. 336(5), 395–398 (2003)
Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast Discrete Curvelet Transforms. Society for Industrial & Applied Mathematics 5(3), 861–899 (2006)
Jianwei, M., Plonka, G.: The Curvelet Transform. IEEE Signal Processing Magazine 27, 118–133 (2010)
Starck, J., Murtagh, F., Candes, E., Donoho, D.: Gray and Color Image contrast Enhancement by the Curvelet Transform. IEEE Trans. Image Processing 12(6) (2003)
Zhen, Z., Jin-Sha, Y., Qiang, G., Ying-Hui, K.: Wavelet Image De-noising Method Based on Noise Standard Deviation Estimation. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Beijing (2007)
Otsu, N.: A Threshold Selection method from Gray level Histograms. IEEE Trans. Syst., Man, Cybern. S2A-9(1), 62–66 (1979)
Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn., pp. 627–679. Prentice-Hall, NJ (2008)
Niemeijer, M., Staal, J., Ginneken, B., Loog, M., Abrà moff, M.D.: Comparative Study of Retinal Vessel Segmentation Methods On a New Publicly Available Database. In: Proc. SPIE—Med. Image., vol. 5370, pp. 648–656 (2004)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. on Image Processing 13(4), 600–612 (2004)
Turaga, D., Yingwei, C., Caviedes, J.: No Reference PSNR Estimation for Compressed Pictures. In: Proceedings International Conference on Image Processing, vol. 6 (2002)
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© 2013 Springer India
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Kumari, R., Bhatnagar, C., Jalal, A.S. (2013). Vascular Tree Segmentation in Fundus Images Using Curvelet Transform. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_102
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DOI: https://doi.org/10.1007/978-81-322-0740-5_102
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
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