Automatic Segmentation of Retina Vessels by Using Zhang Method

Authors: Ehsan Saghapour, Somayeh Zandian

Abstract:

Image segmentation is an important step in image processing. Major developments in medical imaging allow physicians to use potent and non-invasive methods in order to evaluate structures, performance and to diagnose human diseases. In this study, an active contour was used to extract vessel networks from color retina images. Automatic analysis of retina vessels facilitates calculation of arterial index which is required to diagnose some certain retinopathies.

Keywords: Active contour, retinal vessel segmentation, image processing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070457

References:

[1] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, "Blood vessel segmentation methodologies in retinal images - A survey," Computer methods and programs in biomedicine, vol. 108, pp. 407-433, 2012.
[2] L. Espona, M. J. Carreira, M. Ortega, and M. G. Penedo, "A snake for retinal vessel segmentation," Pattern Recognition and Image Analysis, vol. 4478, Lecture Notes Comput. Sci., pp. 178-185, 2007.
[3] L. Espona, M. Carreira, M. Penedo, and M. Ortega, "Retinal vessel tree segmentation using a deformable contour model," 19th International Conference on Pattern Recognition, pp. 1-4, 2008.
[4] B. Al-Diri and A. Hunter, "A ribbon of twins for extracting vessel boundaries," IFMBE Proc. 3rd European Medical and Biological Engineering Conference EMBEC-05, vol. 11, no. 1, 2005.
[5] T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Transactions on Image Processing, vol. 10, pp. 266-277, 2001.
[6] K. Sum and P. Y. S. Cheung, "Vessel extraction under non-uniform illumination: A level set approach," IEEE Transactions on Biomedical Engineering, vol. 55, pp. 358-360, 2008.
[7] Drive: "Digital Retinal Images for Vessel Extraction," 2007 (available from: http://www.isi.uu.nl/Research/Databases/DRIVE/).
[8] Y. Zhang, W. Hsu, and M. L. Lee, "Detection of Retinal Blood Vessels Based on Nonlinear Projections," Journal of Signal Processing Systems, vol. 55, pp. 103-112, 2009.
[9] V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," International journal of computer vision, vol. 22, pp. 61-79, 1997.
[10] B. Al-Diri, A. Hunter, and D. Steel, "An active contour model for segmenting and measuring retinal vessels," IEEE Transactions on Medical Imaging, vol. 28, pp. 1488-1497, 2009.
[11] L. Wang, A. Bhalerao, and R. Wilson, "Analysis of retinal vasculature using a multiresolution hermite model," IEEE Transactions on Medical Imaging, vol. 26, pp. 137-152, 2007.
[12] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, pp. 501-509, 2004.
[13] A. M. Mendonca and A. Campilho, "Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction," IEEE Transactions on Medical Imaging, vol. 25, pp. 1200-1213, 2006.
[14] J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, "Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification," IEEE Transactions on Medical Imaging, vol. 25, pp. 1214-1222, 2006.