Automatic graph cut based segmentation of retinal optic disc by incorporating blood vessel compensation

• Autorzy: Salazar-Gonzalez A. , Li Y , Liu X.
• Języki publikacji: EN

Abstrakty

EN

Glaucoma is one of the main causes of blindness worldwide. Periodical retinal screening is highly recommended in order to detect any sign of the disease and apply the appropriated treatment. Different systems for the analysis of retinal images have been designed in order to assist this process. The segmentation of the optic disc is an important step in the development of a retinal screening system. In this paper we present an unsupervised method for the segmentation of the optic disc. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object. While many other methods have addressed this problem trying to eliminate the vessels, we have incorporated the blood vessel information into our formulation. The blood vessels inside of the optic disc are used to give continuity to the object to segment. Our approach is based on the graph cut technique, where the graph is constructed by considering the relationship between neighbouring pixels and by the likelihood of them belonging to the foreground and background from prior information. Our method was tested on two public datasets, DIARETDB1 and DRIVE. The performance of our method was measured by calculating the overlapping ratio (Oratio), sensitivity and the mean absolute distance (MAD) with respect to the manually labeled images.

Słowa kluczowe

EN

graph cut; retinal optic disc; glaucoma; blindness; retinal screening; segmentation; blood vessel; graph cut technique; DIARETDB1; DRIVE

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2012
• Tom: Vol. 2, No. 3
• Strony: 235--245
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Salazar-Gonzalez A.

• Department of Information Systems and Computing, Brunel University Uxbridge, United Kingdom

autor

Li Y

• Department of Information Systems and Computing, Brunel University Uxbridge, United Kingdom

autor

Liu X.

• Department of Information Systems and Computing, Brunel University Uxbridge, United Kingdom

Bibliografia

• [1] A. Hoover and M. Goldbaum. Locating the optic nerve in retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging, 22(8):951–958, 2003.
• [2] K. W. Tobin, E. Chaum, V. P. Govindasamy, and T. Karnowski. Detection of anatomic structures in human retinal imagery. IEEE Transactions on Medical Imaging, 26(12):1729–1739, 2007.
• [3] A. Youssif, A. Ghalwash, and A. Ghoneim. Optic disc detection from normalized digital fundus images by means of a vessels’s directed matched filter. IEEE Transactions on Medical Imaging, 27(1):11–18, 2008.
• [4] M. Niemeijer, M. D. Abramoff, and B. van Ginneken. Automated localization of the optic disc and the fovea. Proceedings of the 30th Annual International IEEE EMBS conference, pages 3538–3541, 2008.
• [5] T. Kauppi and H. Kalviainen. Simple and robust optic disc localisation using colour decorrelated templates. Proceedings of advanced concepts for intelligence vision systems, pages 719–729, 2008.
• [6] H. Li and O. Chutatape. Automated feature extraction in color retinal images by a model based approach. IEEE Transactions on biomedical engineering, 51(2):246–254, 2004.
• [7] M. Niemeijer, M. D. Abramoff, and B. van Ginneken. Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Transactions on Medical Imaging, 26(1):116–127, 2007.
• [8] X. Yang, P. Morrow, and B. Scotney. Optic nerve head segmentation in hrt images. Proceedings of the international conference on image processing, pages 65–68, 2006.
• [9] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy. Optic nerve head segmentation. IEEE Transactions on Medical Imaging, 23(2):256–264, 2004.
• [10] R. Chrastek, M. Wolf, K. Donath, H. Niemann,D. Paulus, T. Hothorn, B. Lausen, R. Lammer, C. Y. Mardin, and G. Michelson. Automated segmentation of the optic nerve head for diagnosis of glaucoma. Medical Image Analysis, 9(1):297–314,2005.
• [11] D. Welfer, J. Scharcanski, C. Kitamura, M. Dal Pizzol, L. Ludwig, and D. Marinho. Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach. Computers in Biology and Medicine, 40(1):124–137, 2010.
• [12] A. Aquino et al. Detecting the optic disc boundary in digital fundus images using morphological, edge detection and feature extraction techniques. IEEE transactions on medical imaging, 29(10):1860–1869, 2010.
• [13] X. Yang, P. Morrow, and B. Scotney. Optic nerve head segmentation in hrt images. Proceedings of the international conference on image processing, pages 65–68, 2006.
• [14] D Freedman and T Zhang. Interactive graph cut based segmentation with shape priors. Proceedings of the IEEE computer society conference on computer vision and pattern recognition, page 755762, 2005.
• [15] G Slabaugh and G Unal. Graph cuts segmentation using an elliptical shape prior. IEEE International Conference on Image Processing, ICIP, pages II–1222–5, 2005.
• [16] J. Zhu-Jacquot and R. Zabih. Graph cuts segmentation with statistical shape prior for medical image. Proceedings of the third international IEEE conference on signal-image technologies and internetbased system., pages 631–635, 2008.
• [17] A.S Chowdhury, A.K Rudra, M Sen, A Elnakib, and A El-Baz. Cerebral white matter segmentation from mri using probabilistic graph cuts and geometric shape priors. Proceedings of the IEEE 17th international conference on image processing, pages 3649–3652, 2010.
• [18] S.Y Ababneh and M.N Gurcan. An efficient graph cut segmentation for knee bone osteoarthritis medical images. IEEE International Conference on Electro/Information Technology (EIT), pages 1–4, 2010.
• [19] Y. Boykov and M-P. Jolly. Interactive organ segmentation using graph cuts. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 276–286, 2000.
• [20] D.R. Chittajallu, G. Brunner, U. Kurkure, R.P. Yalamanchili, and I.A. Kakadiaris. Fuzzy-cuts: A knowledge-driven graph-based method for medical image segmentation. Proceedings of the twenty third IEEE coference on computer, vision and Pattern recognition., pages 715–722, 2009.
• [21] J. Zhu-Jacquot and R. Zabih. Graph cuts segmentation with statistical shape prior for medical image. Proceedings of the third international IEEE conference on signal-image technologies and internetbased
• [22] Y. Boykov and G. Funka-Lea. Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision, 70(2):109–131, 2006.
• [23] S. Vicente, V. Kolmogorov, and C. Rother. Graph cut based image segmentation with connectivity priors. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008., 1:1–8.
• [24] A. Salazar-Gonzalez, Y. Li, and X. Liu. Retinal blood vessel segmentation via graph cut. In Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision, ICARCV, 1:225–230, 2010.
• [25] A. Vasilevskiy and K. Siddiqi. Flux maximizing geometric flows. IEEE transactions on pattern analysis and machine intelligence., 24(12):1565–1578, 2002.
• [26] V. Kolmogorov and Y. Boykov. What metrics can be approximated by geo-cuts, or global optimization of length/area and flux. Proceedings of the tenth IEEE international conference on computer vision, 2005.
• [27] T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voitilainen, H. Uusitalo, H. Kalviainen, and J. Pietila. Diaretdb1 diabetic retinopathy database and evaluation protocol. In Proceedings of British Machine Vision Conference., 2007.
• [28] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23(4):501–509, 2004.
• [29] Y. Zeng, D. Samaras, W. Chen, and Q. Peng. Topology cuts: a novel min-cut/max-flow algorithm for topology preserving segmentation in nd images. Journal of computer vision and image understanding., 112(1):81–90, 2008.
• [30] T. Walter, J-C Klein, P. Massin, and A. Erginay. A contribution of image processing to the diagnosis of diabetic retinopathy: Detection of exudates in color fundus images of the human retina. IEEE Transactions on Medical Imaging, 21(10):1236–1243, 2002.