Abstract
Retinal blood vessels play an imperative role in detection of many ailments, such as cardiovascular diseases, hypertension, and diabetic retinopathy. The automated way of segmenting vessels from retinal images can help in early detection of many diseases. In this paper, we propose a framework based on hybrid feature set and hierarchical classification approach to segment blood vessels from digital retinal images. Firstly, we apply bidirectional histogram equalization on the inverted green channel to enhance the fundus image. Six discriminative feature extraction methods have been employed comprising of local intensities, local binary patterns, histogram of gradients, divergence of vector field, high-order local autocorrelations, and morphological transformation. The selection of feature sets has been carried out by classifying vessel and background pixels using random forests and evaluating the segmentation performance for each category of features. The selected feature sets are then used in conjunction with our proposed hierarchical classification approach to segment the vessels. The proposed framework has been tested on the DRIVE, STARE, and CHASEDB1 which are the benchmark datasets for retinal vessel segmentation methods. The results obtained from the experimental analysis show that the proposed framework can achieve better results than most state-of-the-art methods.
Similar content being viewed by others
References
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Programs Biomed. 108, 407–433 (2012)
Marín, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A New supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30, 146–158 (2011). https://doi.org/10.1109/TMI.2010.2064333
Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19, 46–57 (2015). https://doi.org/10.1016/j.media.2014.08.002
Soares, J.V.B., Leandro, J.J.G., Cesar, 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, 1214–1222 (2006). https://doi.org/10.1109/TMI.2006.879967
Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19, 203–210 (2000). https://doi.org/10.1109/42.845178
Owen, C.G., Rudnicka, A.R., Mullen, R., Barman, S.A., Monekosso, D., Whincup, P.H., Ng, J., Paterson, C.: Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program. Investig. Opthalmol. Vis. Sci. 50, 2004 (2009)
Hassan, G., Hassanien, A.E.: Retinal fundus vasculature multilevel segmentation using whale optimization algorithm. Signal Image Video Process 12, 263–270 (2018). https://doi.org/10.1007/s11760-017-1154-z
Staal, J., Abramoff, 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, 501–509 (2004). https://doi.org/10.1109/TMI.2004.825627
Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf Technol. Biomed. 14, 1267–1274 (2010)
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59, 2538–2548 (2012). https://doi.org/10.1109/TBME.2012.2205687
Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang, G.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149, 708–717 (2015). https://doi.org/10.1016/j.neucom.2014.07.059
Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imaging Graph. 34, 213–227 (2010)
Zhang, L., Fisher, M., Wang, W.: Retinal vessel segmentation using multi-scale textons derived from keypoints. Comput. Med. Imaging Graph. 45, 47–56 (2015). https://doi.org/10.1016/j.compmedimag.2015.07.006
Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z., Liang, Y.: Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput. Med. Imaging Graph. 55, 68–77 (2017)
Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35, 109–118 (2016). https://doi.org/10.1109/TMI.2015.2457891
Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention, pp. 132–139. Springer, Berlin (2016)
Hu, K., Zhang, Z., Niu, X., Zhang, Y., Cao, C., Xiao, F., Gao, X.: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309, 179–191 (2018)
Abdel-Hamid, L., El-Rafei, A., El-Ramly, S., Michelson, G.: Performance dependency of retinal image quality assessment algorithms on image resolution: analyses and solutions. Signal Image Video Process 12, 9–16 (2018)
Kim, Yeong-Taeg: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43, 1–8 (1997). https://doi.org/10.1109/30.580378
Ooi, C., Pik Kong, N., Ibrahim, H.: Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans. Consum. Electron. 55, 2072–2080 (2009). https://doi.org/10.1109/TCE.2009.5373771
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. In: Computer Vision and Pattern Recognition, 1996. Proceedings CVPR’96, 1996 IEEE Computer Society Conference, vol 30, pp. 465–470 (1996). https://doi.org/10.1023/a:1008097225773
Lam, B.S.Y., Hong, Y.: A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans. Med. Imaging 27, 237–246 (2008). https://doi.org/10.1109/TMI.2007.909827
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), pp. 886–893. IEEE (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623
Otsu, N., Kurita, T.: A New Scheme for Practical Flexible and Intelligent Vision Systems. In: Proceedings of IAPR Workshop on Computer VisionSpecial Hardware and Industrial Applications, pp. 431–435 (1988)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Saeedi, J., Ahadi, S.M., Faez, K.: Robust voice activity detection directed by noise classification. Signal Image Video Process 9, 561–572 (2015)
Hiary, H., Alomari, R.S., Saadah, M., Chaudhary, V.: Automated segmentation of stromal tissue in histology images using a voting Bayesian model. Signal Image Video Process 7, 1229–1237 (2013). https://doi.org/10.1007/s11760-012-0393-2
Vega, R., Sanchez-Ante, G., Falcon-Morales, L.E., Sossa, H., Guevara, E.: Retinal vessel extraction using lattice neural networks with dendritic processing. Comput. Biol. Med. 58, 20–30 (2015). https://doi.org/10.1016/j.compbiomed.2014.12.016
Wu, A., Xu, Z., Gao, M., Buty, M., Mollura, D.J.: Deep vessel tracking: A generalized probabilistic approach via deep learning. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1363–1367. IEEE (2016)
Fraz, M.M., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int. J. Comput. Assist. Radiol. Surg. 9, 795–811 (2014)
Miri, M.S., Mahloojifar, A.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Biomed. Eng. 58, 1183–1192 (2011). https://doi.org/10.1109/TBME.2010.2097599
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Owen, C.G., Rudnicka, A.R., Barman, S.A.: Retinal vessel extraction using first-order derivative of gaussian and morphological processing. In: International Symposium on Visual Computing, pp. 410–420 (2011)
Qian Zhao, Y., Hong Wang, X., Fang Wang, X., Shih, F.Y.: Retinal vessels segmentation based on level set and region growing. Pattern Recognit. 47, 2437–2446 (2014)
Lázár, I., Hajdu, A.: Segmentation of retinal vessels by means of directional response vector similarity and region growing. Comput. Biol. Med. 66, 209–221 (2015)
Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64, 16–27 (2017). https://doi.org/10.1109/TBME.2016.2535311
You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit. 44, 2314–2324 (2011)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors of this paper declare no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Khowaja, S.A., Khuwaja, P. & Ismaili, I.A. A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification. SIViP 13, 379–387 (2019). https://doi.org/10.1007/s11760-018-1366-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-018-1366-x