Abstract
As an early indication of diseases including diabetes, hypertension, and retinopathy of prematurity, structural study of retinal vessels becomes increasingly important. These studies have driven the need toward accurate and consistent tracing of retinal blood vessel tree structures from fundus images in an automated manner. In this paper we propose a two-step pipeline: First, the retinal vessels are segmented with the preference of preserving the skeleton network, i.e., retinal segmentation with a high recall. Second, a novel tracing algorithm is developed where the tracing problem is uniquely mapped to an inference problem in probabilistic graphical models. This enables the exploitation of well-developed inference toolkit in graphical models. The competitive performance of our method is verified on publicly available datasets comparing to the state-of-the-arts.
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References
Sun, C., Wang, J., Mackey, D., Wong, T.: Retinal vascular caliber: Systemic, environmental, and genetic associations. Survey of Ophthalmology 54(1), 74–95 (2009)
Wang, J., Liew, G., Klein, R., Rochtchina, E., Knudtson, M., Klein, B., Wong, T., Burlutsky, G., Mitchell, P.: Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations. European Heart Journal 28(16), 1984–1992 (2007)
Martinez-Perez, M., Hughes, A., Stanton, A.H., Thom, S., Chapman, N., Bharath, A., Parker, K.: Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans. Biomed. Eng. 49(8), 912–917 (2002)
King, L., Stanton, A., Sever, P., Thom, S., Hughes, A.: Arteriolar length-diameter (l:d) ratio: a geometric parameter of the retinal vasculature diagnostic of hypertension. J. Hum. Hypertens. 10(6), 417–424 (1996)
Mendonca, A., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imag. 25(9), 1200–1213 (2006)
Garg, S., Sivaswamy, J., Chandra, S.: Unsupervised curvature-based retinal vessel segmentation. In: International Symposium on Biomedical Imaging, pp. 1200–1213 (2007)
Espona, L., Carreira, M., Penedo, M., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: International Conference on Pattern Recognition (2008)
Martinez-Perez, M., Hughes, A., Thom, S., Bharath, A., Parker, K.: Segmentation of blood vessels from red-free and uorescein retinal images. Med. Image Anal. 11(1), 47–61 (2007)
Soares, J., Leandro, J., Cesar, R., Jelinek, H., Cree, M.: Retinal vessel segmentation using the 2d gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 25(9), 1214–1222 (2007)
Marin, D., Aquino, A., Gegundez-Arias, M., Brav, J.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imag. 30(1), 146–158 (2011)
Bankhead, P., Scholfield, C., McGeown, J., Curtis, T.: Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 7(3), e32435 (2012)
Wang, L., Bhalerao, A.: Model based segmentation for retinal fundus images. In: Scandinavian Conference on Image Analysis, pp. 422–429 (2003)
Xu, X., Niemeijer, M., Song, Q., Sonka, M., Garvin, M., Reinhardt, J., Abrãmoff, M.: Vessel boundary delineation on fundus images using graph-based approach, pp. 1184–1191 (2011)
Can, A., Shen, H., Turner, J., Tanenbaum, H., Roysam, B., Roysam, B.: Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms, pp. 125–138 (1999)
Grisan, E., Pesce, A., Giani, A., Foracchia, M., Ruggeri, A.: A new tracking system for the robust extraction of retinal vessel structure. IEEE Engineering in Medicine and Biology Society 1, 1620–1623 (2004)
Bekkers, E., Duits, R., Romeny, B., Berendschot, T.: A new retinal vessel tracking method based on orientation scores. CoRR abs/1212.3530 (2012)
Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging 28(9), 1488–1497 (2009)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Gamarnik, D., Katz, D.: Correlation decay and deterministic fptas for counting colorings of a graph. J. Discrete Algorithms 12, 29–47 (2012)
Gamarnik, D., Goldberg, D., Weber, T.: Correlation decay in random decision networks. CoRR abs/0912.0338 (2009)
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23(4), 501–509 (2004)
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imag. 26(10), 1357–1365 (2007)
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De, J., Ma, T., Li, H., Dash, M., Li, C. (2013). Automated Tracing of Retinal Blood Vessels Using Graphical Models. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_27
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DOI: https://doi.org/10.1007/978-3-642-38886-6_27
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