Abstract
The research of retinal vessel segmentation prevails since retinal vessels well indicate the diseases, such as diabetic retinopathy, glaucoma and hypertension. This paper proposes an efficient CNN-CRF framework to segment the vessels from digital retinal images. Our approach combines the prediction ability of CNN and the segmentation ability of CRF, and trains an end-to-end deep learning segmentation model for retinal images. Unlike pixel-wise segmentation, our network is able to segment one image during once network forward computation. When applying our CNN-CRF to the DRIVE database, the average accuracy achieves 0.9536 with the average recall rate of 0.7508, outperforming the state-of-art approaches. And our approach requires only 0.53 s per image, the fastest among deep learning approaches.
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References
Al-Rawi, M., Karajeh, H.: Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Comput. Methods Programs Biomed. 87(3), 248–253 (2007)
Anzalone, A., Bizzarri, F., Parodi, M., Storace, M.: A modular supervised algorithm for vessel segmentation in red-free retinal images. Comput. Biol. Med. 38(8), 913–922 (2008)
Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable cosfire filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)
Bankhead, P., Scholfield, C.N., McGeown, J.G., Curtis, T.M.: Fast retinal vessel detection and measurement using wavelets and edge location refinement. PloS one 7(3), e32435 (2012)
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)
Espona, L., Carreira, M.J., Penedo, M., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: 19th International Conference on Pattern Recognition 2008, ICPR 2008, pp. 1–4. IEEE (2008)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
Li, Q., Xie, L., Zhang, Q., Qi, S., Liang, P., Zhang, H., Wang, T.: A supervised method using convolutional neural networks for retinal vessel delineation. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 418–422. IEEE (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Maji, D., Santara, A., Mitra, P., Sheet, D.: Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images. arXiv preprint arXiv:1603.04833 (2016)
Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Med. Image Anal. 11(1), 47–61 (2007)
Melinščak, M., Prentašić, P., Lončarić, S.: Retinal vessel segmentation using deep neural networks. In: VISAPP 2015 (10th International Conference on Computer Vision Theory and Applications) (2015)
Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)
Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004, pp. 648–656. International Society for Optics and Photonics (2004)
Orlando, J.I., Blaschko, M.: Learning fully-connected CRFs for blood vessel segmentation in retinal images. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 634–641. Springer, Cham (2014). doi:10.1007/978-3-319-10404-1_79
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)
Sangeetha, K., Karthiga, R., Jeyanthi, K.: Advanced analysis of anatomical structures using hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis. In: 2012 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–12. IEEE (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Soares, J.V., Leandro, J.J., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Drive database. http://www.isi.uu.nl/Research/Databases/DRIVE/
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Xu, L., Luo, S.: A novel method for blood vessel detection from retinal images. Biomed. Eng. Online 9(1), 14 (2010)
Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc. 10(7), 1010–1019 (2001)
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)
Acknowledgment
The authors would like to thank all the reviewers for their insightful comments. This work was supported by the National Natural Science Foundation of China (Grant No. 61305033, 61273256 and 6157021026), Fundamental Research Funds for the Central Universities (ZYGX2014Z009).
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Luo, Y., Yang, L., Wang, L., Cheng, H. (2017). Efficient CNN-CRF Network for Retinal Image Segmentation. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_17
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DOI: https://doi.org/10.1007/978-981-10-5230-9_17
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