Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

1. [1] C. A. Lupascu, D. Tegolo, and E. Trucco, “FABC: Retinal vessel segmentation using adaboost,” IEEE Trans. on Information Technology in Biomedicine. Vol.14, No.5, pp. 1267-1274, 2010.
2. [2] Y. Koga, A. Yamamoto, H. Kim, J. K. Tan, and S. Ishikawa, “Detection of artery regions in lower extremity arteries from non-enhanced MR imaging based on particle filter algorithms,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.17, No.2, pp. 318-323, 2013.
3. [3] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “Blood vessel segmentation methodologies in retinal images – A survey,” Computer Methods & Programs in Biomedicine, Vol.108, No.1, pp. 407-433, 2012.
4. [4] F. Jusoh, H. Haron, R. Ibrahim, and M. Z. C. Azemin, “An overview of retinal blood vessel segmentation,” Advanced Computer and Communication Engineering Technology, Vol.362, pp. 63-71, 2015.
5. [5] A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. on Medical Imaging, Vol.49, No.2, pp. 168-172, 2000.
6. [6] L. Gamg, O. Chutatape, and S. M. Krishnan, “Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter,” IEEE Trans. on Biomedical Engineering, Vol.49, No.2, pp. 168-172, 2002.
7. [7] W. S. Oliveira, T. I. Ren, and G. D. C. Cavalcanti, “An unsupervised segmentation method for retinal vessel using combined filters,” Proc. of IEEE 24th Int. Conf. on Tools with Artificial Intelligence, pp. 750-756, 2012.
8. [8] N. P. Singh and R. Srivastava, “Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter,” Computer Methods and Programs in Biomedicine, Vol.129, pp. 40-50, 2016.
9. [9] F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. on Image Processing, Vol.10, No.7, pp. 1010-1019, 2001.
10. [10] A. M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the direction of centerlines and morphological reconstruction,” IEEE Trans. on Medical Imaging, Vol.25, No.9, pp. 1200-1213, 2006.
11. [11] M. M. Fraz, S. A. Barman, P. Remagnino, A. Hoppe, A. basit, B. Uyyanonvara, A. R. Rudnickc, and C. G. Owen, “An approach to localize the retinal blood vessels using bit planes and centerline detection,” Computer Methods & Programs in Biomedicine, Vol.108, No.2, pp. 600-616, 2012.
12. [12] F. K. H. Quek and C. Kirbas, “Vessel extraction in medical images by wave-propagation and traceback,” IEEE Trans. on Medical Imaging, Vol.20, No.2, pp. 117-131, 2001.
13. [13] A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” Medical Image Computing and Computer-Assisted Intervention, pp. 130-137, 1496.
14. [14] B. S. Dai, W. Bu, X. Q. Wu, and Y. L. Zheng, “Retinal blood vessel detection using multiscale line filter and phase congruency,” Proc. of Int. Conf. on Image Processing, Computer Vision, & Pattern Recognition, pp. 1-7, 2013.
15. [15] R. Masooomi, A. Ahmadifard, and A. Mohtadizadeh, “Retinal vessel segmentation using non-subsampled contourlet transform and multi-scale line detection,” Proc. of Iranian Conf. on Intelligent Systems, pp. 1-5, 2014.
16. [16] W. Li, A. Bhalerao, and R. Wilson, “Analysis of retinal vasculature using a multiresolution hermite model,” IEEE Trans. on Medical Imaging, Vol.26, No.2, pp. 137-152, 2007.
17. [17] B. S. Y. Lam and Y. Hong, “A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields,” IEEE Trans. on Medical Imaging, Vol.27, No.2, pp. 237-246, 2008.
18. [18] B. S. Y. Lam, G. Yongsheng, and A. W. C. Liew, “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. on Medical Imaging, Vol.29, No.7, pp. 1369-1381, 2010.
19. [19] H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. on Biomedical Engineering, Vol.54, No.8, pp. 1427-1435, 2007.
20. [20] B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Trans. on Medical Imaging, Vol.28, No.9, pp. 1488-1497, 2009.
21. [21] P. R. Wankhede and K. B. Khanchandani, “Retinal blood vessel segmentation using graph cut analysis,” Proc. of Int. Conf. on Industrial Instrumentation and Control, pp. 1429-1432, 2015.
22. [22] K. W. Sum and P. Y.. S. Cheung, “Vessel extraction under non-uniform illumination: A level set approach,” IEEE Trans. on Biomedical Engineering, Vol.55, No.1, pp. 358-360, 2008.
23. [23] M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. of SPIE, Vol.5370, pp. 648-665, 2004.
24. [24] 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 Trans. on Medical Imaging, Vol.23, No.4, pp. 501-509, 2004.
25. [25] J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. on Medical Imaging, Vol.25, No.9, pp. 1214-1222, 2006.
26. [26] E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. on Medical Imaging, Vol.26, No.10, pp. 1357-1365, 2007.
27. [27] D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. on Medical Imaging, Vol.30, No.1, pp. 146-158, 2011.
28. [28] X. You, Q. Peng, Y. Yuan, Y. Cheung, and J. Lei, “Segmentation of retinal blood vessels using the radial projection and semi-supervised approach,” Pattern Recognition, Vol.44, pp. 2314-2324, 2011.
29. [29] M. Nandy and M. Banerjee, “Retinal vessel segmentation using Gabor filter and artificial neural network,” Proc. of 3rd Int. Conf. on Emerging Applications of Information Technology, pp. 157-160, 2012.
30. [30] M. Ceylan and H. Yasar, “Blood vessel extraction from retinal images using complex wavelet transform and complex-valued artificial neural network,” Proc. of 36th Int. Conf. on Telecommunications and Signal Processing, pp. 822-825, 2013.
31. [31] C. Ding, Y. Xia, and Y. Li, “Supervised segmentation of vasculature in retinal images using neural networks,” Proc. of IEEE Int. Conf. on Orange Technologies, pp. 49-52, 2014.
32. [32] M. Melinscak, P. Prentasic, and S. Loncaric, “Retinal vessel segmentation using deep neural networks,” Proc. of 10th Int. Conf. on Computer Vision Theory and Applications, pp. 577-582, 2015.
33. [33] Q. L. Li, B. W. Feng, L. P. Xie, P. Liang, H. S. Zhang, and T. F. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. on Medical Imaging, Vol.35, No.1, pp. 109-118, 2016.
34. [34] K. M. He, J. Sun, and X. O. Tang, “Guided image filtering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.35, No.6, pp. 1397-1409, 2013.
35. [35] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Trans. on Biomedical Engineering, Vol.59, No.9, pp. 2538-2548, 2012.
36. [36] A. Frangi, W. J. Niessen, K. Vincken, and M. Viergever, “Multiscale vessel enhancement filtering,” Medical Image Computing and Computer-Assisted Intervention, Vol.1496, pp. 130-137, 2006.
37. [37] E. Aswini, S. Divya, S. Kardheepan, and T. Manikandan, “Mathematical morphology and bottom-hat filtering approach for crack detection on relay surfaces,” Proc. of IEEE Int. Conf. on Smart Structures and Systems, pp. 108 -113, 2013.
38. [38] G. B. Huang, Q. Y. Zhu, K. Z. Mao, C. K. Siew, P. Saratchandran, and N. Sundararajan, “Can threshold networks be trained directly?,” IEEE Trans. on Circuits & Systems II Express Briefs, Vol.53, No.3, pp. 187-191, 2006.
39. [39] G. B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. on Systems Man & Cybernetics Part B Cybernetics, Vol.42, No.42, pp. 513-529, 2012.
40. [40] http://www.isi.uu.nl/Research/Databases/DRIVE [accessed May 15, 2015]
41. [41] http://www.ces.clemson.edu/_ahoover/stare/probing/index.html/ [accessed May 1, 2015]