Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31106
Attention Based Fully Convolutional Neural Network for Simultaneous Detection and Segmentation of Optic Disc in Retinal Fundus Images

Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara


Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: Ocular Diseases, retinal fundus image, optic disc detection and segmentation, fully convolutional network, overlap measure

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 207


[1] R. R. Bourne, S. R. Flaxman, T. Braithwaite, M. V. Cicinelli, A. Das, J. B. Jonas, J. Keeffe, J. H. Kempen, J. Leasher, H. Limburg et al., “Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis,” The Lancet Global Health, vol. 5, no. 9, pp. e888–e897, 2017.
[2] D. Pascolini and S. P. Mariotti, “Global estimates of visual impairment: 2010,” British Journal of Ophthalmology, vol. 96, no. 5, pp. 614–618, 2012.
[3] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Transactions on medical Imaging, vol. 23, no. 2, pp. 256–264, 2004.
[4] G. D. Joshi, J. Sivaswamy, and S. Krishnadas, “Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment,” IEEE transactions on medical imaging, vol. 30, no. 6, pp. 1192–1205, 2011.
[5] J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE Transactions on Medical Imaging, vol. 32, no. 6, pp. 1019–1032, 2013.
[6] R. J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto, and A. Hajdu, “Combining algorithms for automatic detection of optic disc and macula in fundus images,” Computer Vision and Image Understanding, vol. 116, no. 1, pp. 138–145, 2012.
[7] R. Estrada, C. Tomasi, S. C. Schmidler, and S. Farsiu, “Tree topology estimation,” IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 8, pp. 1688–1701, 2015.
[8] A. Aquino, M. E. Geg´undez-Arias, and D. Mar´ın, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE transactions on medical imaging, vol. 29, no. 11, pp. 1860–1869, 2010.
[9] S. Barrett, E. Naess, and T. Molvik, “Employing the hough transform to locate the optic disk.” Biomedical sciences instrumentation, vol. 37, pp. 81–86, 2001.
[10] C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” British Journal of Ophthalmology, vol. 83, no. 8, pp. 902–910, 1999.
[11] M. Blanco, M. G. Penedo, N. Barreira, M. Penas, and M. J. Carreira, “Localization and extraction of the optic disc using the fuzzy circular hough transform,” in International Conference on Artificial Intelligence and Soft Computing. Springer, 2006, pp. 712–721.
[12] A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE transactions on medical imaging, vol. 22, no. 8, pp. 951–958, 2003.
[13] M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE transactions on medical imaging, vol. 23, no. 10, pp. 1189–1195, 2004.
[14] A. A.-H. A.-R. Youssif, A. Z. Ghalwash, and A. A. S. A.-R. Ghoneim, “Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter,” IEEE Transactions on Medical imaging, vol. 27, no. 1, pp. 11–18, 2008.
[15] A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Comparison of colour spaces for optic disc localisation in retinal images,” in Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 1. IEEE, 2002, pp. 743–746.
[16] H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Transactions on biomedical engineering, vol. 51, no. 2, pp. 246–254, 2004.
[17] A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” Journal of Medical Imaging, vol. 1, no. 2, pp. 024 001–024 001, 2014.
[18] I. Soares, M. Castelo-Branco, and A. M. Pinheiro, “Optic disc localization in retinal images based on cumulative sum fields,” IEEE journal of biomedical and health informatics, vol. 20, no. 2, pp. 574–585, 2016.
[19] D. Zhang and Y. Zhao, “Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics,” IEEE journal of biomedical and health informatics, vol. 20, no. 1, pp. 333–342, 2016.
[20] S. Roychowdhury, D. D. Koozekanani, S. N. Kuchinka, and K. K. Parhi, “Optic disc boundary and vessel origin segmentation of fundus images,” IEEE journal of biomedical and health informatics, vol. 20, no. 6, pp. 1562–1574, 2016.
[21] G. Papandreou, I. Kokkinos, and P.-A. Savalle, “Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection,” in Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015, pp. 390–399.
[22] X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille, “Detect what you can: Detecting and representing objects using holistic models and body parts,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1971–1978.
[23] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 3213–3223.
[24] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” arXiv preprint arXiv:1606.00915, 2016.
[25] O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz et al., “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999, 2018.
[26] S. Jetley, N. A. Lord, N. Lee, and P. H. Torr, “Learn to pay attention,” arXiv preprint arXiv:1804.02391, 2018.
[27] L. Fan, W.-C. Wang, F. Zha, and J. Yan, “Exploring new backbone and attention module for semantic segmentation in street scenes,” IEEE Access, vol. 6, pp. 71 566–71 580, 2018.
[28] S. Woo, J. Park, J.-Y. Lee, and I. So Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3–19.
[29] D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[30] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” preprint arXiv:1207.0580, 2012.
[31] S. Morales, V. Naranjo, J. Angulo, and M. Alca˜niz, “Automatic detection of optic disc based on pca and mathematical morphology,” IEEE transactions on medical imaging, vol. 32, no. 4, pp. 786–796, 2013.
[32] A. G. Salazar-Gonzalez, Y. Li, and X. Liu, “Optic disc segmentation by incorporating blood vessel compensation,” in Computational Intelligence In Medical Imaging (CIMI), 2011 IEEE Third International Workshop On. IEEE, 2011, pp. 1–8.
[33] D. Welfer, J. Scharcanski, C. M. Kitamura, M. M. Dal Pizzol, L. W. Ludwig, and D. R. Marinho, “Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach,” Computers in Biology and Medicine, vol. 40, no. 2, pp. 124–137, 2010.
[34] D. Marin, M. E. Gegundez-Arias, A. Suero, and J. M. Bravo, “Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images,” Computer methods and programs in biomedicine, vol. 118, no. 2, pp. 173–185, 2015.
[35] H. Yu, E. S. Barriga, C. Agurto, S. Echegaray, M. S. Pattichis, W. Bauman, and P. Soliz, “Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets,” IEEE Transactions on information technology in biomedicine, vol. 16, no. 4, pp. 644–657, 2012.