This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.
References
-
-
1)
-
14. Acharya, U.R., Ng, E.Y.K., Eugene, L.W.J., et al: ‘Decision support system for the glaucoma using Gabor transformation’, Biomed. Signal Process. Control, 2015, 15, pp. 18–26 (doi: 10.1016/j.bspc.2014.09.004).
-
2)
-
10. Maheshwari, S., Pachori, R.B., Acharya, U.R.: ‘Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images’, IEEE J. Biomed. Health Inf., 2017, 21, (3), pp. 803–813 (doi: 10.1109/JBHI.2016.2544961).
-
3)
-
18. Gagnon, L., Lalonde, M., Beaulieu, M., et al: ‘Procedure to detect anatomical structures in optical fundus images’, Proc. SPIE Med. Imaging, Image Process., 2001, 4322, pp. 1218–1225.
-
4)
-
24. Roychowdhury, S., Koozekanani, D.D., Kuchinka, S.N., et al: ‘Optic disc boundary and vessel origin segmentation of fundus images’, IEEE J. Biomed. Health Inf., 2016, 20, (6), pp. 1562–1574 (doi: 10.1109/JBHI.2015.2473159).
-
5)
-
11. Radim, K., Jan, J.: ‘Detection of glaucomatous eye via colour fundus images using fractal dimensions’, Radioengineering, 2008, 17, (3), pp. 109–114.
-
6)
-
6. Mittapalli, P.S., Kande, G.B.: ‘Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma’, Biomed. Signal Process. Control, 2016, 24, pp. 34–46 (doi: 10.1016/j.bspc.2015.09.003).
-
7)
-
1. Quigley, H.A., Broman, A.T.: ‘The number of people with glaucoma worldwide in 2010 and 2020’, Br. J. Ophthalmol., 2006, 90, pp. 262–267 (doi: 10.1136/bjo.2005.081224).
-
8)
-
28. Dai, B., Wu, X., Bu, W.: ‘Optic disc segmentation based on variational model with multiple energies’, Pattern Recognit., 2017, 64, pp. 226–235 (doi: 10.1016/j.patcog.2016.11.017).
-
9)
-
38. Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., et al: ‘Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation’. Proc. IEEE Int. Symp. on Biomedical Imaging(ISBI), 2014, pp. 53–56.
-
10)
-
41. Salazar-Gonzalez, A., Kaba, D., Li, Y., et al: ‘Segmentation of the blood vessels and optic disk in retinal images’, IEEE J. Biomed. Health Inf., 2014, 18, (6), pp. 1874–1886 (doi: 10.1109/JBHI.2014.2302749).
-
11)
-
42. Diaz-Pernil, D., Fondon, I., Pena-Cantillana, F., et al: ‘Fully automatized parallel segmentation of the optic disc in retinal fundus images’, Pattern Recognit. Lett., 2016, 83, pp. 99–107 (doi: 10.1016/j.patrec.2016.04.025).
-
12)
-
13. Dua, S., Acharya, U.R., Chowriappa, P., et al: ‘Wavelet-based energy features for glaucomatous image classification’, IEEE Trans. Inf. Tech. Biomed., 2012, 16, (1), pp. 80–87 (doi: 10.1109/TITB.2011.2176540).
-
13)
-
13. Acharya, U.R., Dua, S., Du, X., et al: ‘Automated diagnosis of glaucoma using texture and higher order spectra features’, IEEE Trans. Inf. Technol. Biomed., 2011, 15, (3), pp. 449–455 (doi: 10.1109/TITB.2011.2119322).
-
14)
-
36. Staal, J., Abramoff, M., Niemeijer, M., et al: ‘Ridge-based vessel segmentation in color images of the retina’, IEEE Trans. Med. Imaging, 2004, 23, (4), pp. 501–509 (doi: 10.1109/TMI.2004.825627).
-
15)
-
31. Yin, L.K., Rajeswari, M.: ‘Random walker with improved weighting function for interactive medical image segmentation’, Biomed. Mater. Eng., 2014, 24, (6), pp. 3333–3341.
-
16)
-
12. Maheshwari, S., Pachori, R.B., Kanhangad, V., et al: ‘Iterative variational mode decomposition based automated detection of glaucoma using fundus images’, Comput. Biol. Med., 2017, 88, pp. 142–149 (doi: 10.1016/j.compbiomed.2017.06.017).
-
17)
-
35. Fogel, I., Dov, S.: ‘Gabor filters as texture discriminator’, Biol. Cybern., 1989, 61, (2), pp. 103–113 (doi: 10.1007/BF00204594).
-
18)
-
21. Sekhar, S., Al-Nuaimy, W., Nandi, A.K.: ‘Automated localisation of retinal optic disc using Hough transform’. Proc. IEEE Int. Symp. on Biomedical Imaging (ISBI), 2008, pp. 1577–1580.
-
19)
-
23. Pallawala, P., Hsu, W., Lee, M., et al: ‘Automated optic disc localization and contour detection using ellipse fitting and wavelet transform’. Proc. European Conf. Computer Vision, 2004, pp. 139–151.
-
20)
-
43. Muramatsu, C., Nakagawa, T., Sawada, A., et al: ‘Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modelling and pixel classification methods’, Comput. Methods Prog. Biomed., 2011, 101, (1), pp. 23–32 (doi: 10.1016/j.cmpb.2010.04.006).
-
21)
-
2. Lim, T.C., Chattopadhyay, S., Acharya, U.R.: ‘A survey and comparative study on the instruments for glaucoma detection’, Med. Eng. Phys., 2012, 34, (2), pp. 129–139 (doi: 10.1016/j.medengphy.2011.07.030).
-
22)
-
34. Gong, Y.: ‘Spectrally regularized surfaces’. , 2015.
-
23)
-
4. Joshi, G.D., Sivaswamy, J., Krishnadas, S.R.: ‘Optic disk and cup segmentation from monocular colour retinal images for glaucoma assessment’, IEEE Trans. Med. Imaging, 2011, 30, (6), pp. 1192–1205 (doi: 10.1109/TMI.2011.2106509).
-
24)
-
33. Panda, R., Puhan, N.B., Panda, G.: ‘Robust and accurate optic disk localization using vessel symmetry line measure in fundus images’, Biocyber. Biomed. Eng., 2017, 37, (3), pp. 466–476 (doi: 10.1016/j.bbe.2017.05.008).
-
25)
-
8. Noronha, K.P., Acharya, U.R., Nayak, K.P., et al: ‘Automated classification of glaucoma stages using higher order cumulant features’, Biomed. Signal Process. Control, 2014, 10, pp. 174–183 (doi: 10.1016/j.bspc.2013.11.006).
-
26)
-
6. Lowell, J., Hunter, A., Steel, D., et al: ‘Optic nerve head segmentation’, IEEE Trans. Med. Imaging, 2004, 23, (2), pp. 256–264 (doi: 10.1109/TMI.2003.823261).
-
27)
-
4. Lalonde, M., Beaulieu, M., Gagnon, L.: ‘Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching’, IEEE Trans. Med. Imaging, 2001, 20, (11), pp. 1193–1200 (doi: 10.1109/42.963823).
-
28)
-
37. Kauppi, T., Kalesnykiene, V., Kamarainen, J., et al: ‘Diaretdb1 diabetic retinopathy database and evaluation protocol’. Proc. British Machine Vision Conf., 2007, pp. 1–10.
-
29)
-
9. Mookiah, M.R.K., Acharya, U.R., Lim, C.M., et al: ‘Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features’, Knowl.-Based Syst., 2012, 33, pp. 73–82 (doi: 10.1016/j.knosys.2012.02.010).
-
30)
-
26. Wong, D., Liu, J., Lim, J., et al: ‘Level set based automatic cup-to-disc ratio determination using retinal fundus images in argali’. Proc. Engineering in Medicine and Biology Society, 2008, pp. 2266–2269.
-
31)
-
30. Yang, X., Su, Y., Duan, R., et al: ‘Cardiac image segmentation by random walks with dynamic shape constraint’, IET Comput. Vis., 2016, 10, (1), pp. 79–86 (doi: 10.1049/iet-cvi.2014.0450).
-
32)
-
17. Lu, S.: ‘Accurate and efficient optic disc detection and segmentation by a circular transformation’, IEEE Trans. Med. Imaging, 2011, 30, (12), pp. 2126–2133 (doi: 10.1109/TMI.2011.2164261).
-
33)
-
34)
-
16. Mary, M., Rajsingh, E., Naik, G.: ‘Retinal fundus image analysis for diagnosis of glaucoma: a comprehensive survey’, IEEE Access, 2016, 4, pp. 4327–4354 (doi: 10.1109/ACCESS.2016.2596761).
-
35)
-
17. Lahmiri, S.: ‘High-frequency-based features for low and high retina haemorrhage classification’, Healthc. Technol. Lett., 2017, 4, (1), pp. 20–24 (doi: 10.1049/htl.2016.0067).
-
36)
-
3. Xu, J., Chutatape, O., Sung, E., et al: ‘Optic disk feature extraction via modified deformable model technique for glaucoma analysis’, Pattern Recognit., 2007, 40, pp. 2063–2076 (doi: 10.1016/j.patcog.2006.10.015).
-
37)
-
11. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 1–17 (doi: 10.1109/TPAMI.2006.233).
-
38)
-
20. Aquino, A., Gegundez, M.E., Marin, D.: ‘Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques’, IEEE Trans. Med. Imaging, 2010, 29, (11), pp. 1860–1869 (doi: 10.1109/TMI.2010.2053042).
-
39)
-
5. Chrástek, R., Wolf, M., Donath, K., et al: ‘Automated segmentation of the optic nerve head for diagnosis of glaucoma’, Med. Image Anal., 2005, 9, (4), pp. 297–314 (doi: 10.1016/j.media.2004.12.004).
-
40)
-
15. Acharya, U.R., Bat, S., Koh, J.E.W., et al: ‘A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images’, Comput. Biol. Med., 2017, 88, (1), pp. 72–83 (doi: 10.1016/j.compbiomed.2017.06.022).
-
41)
-
25. Lee, S., Brady, M.: ‘Optic disk boundary detection’. Proc. British Machine Vision Conf., 1991, pp. 359–362.
-
42)
-
32. Panda, R., Puhan, N.B., Panda, G.: ‘New binary Hausdorff symmetry measure based seeded region growing for retinal vessel segmentation’, Biocyber. Biomed. Eng., 2016, 36, (1), pp. 119–129 (doi: 10.1016/j.bbe.2015.10.005).
-
43)
-
27. Welfer, D., Scharcanski, J., Kitamura, C., Pizzol, M., Ludwig, L., Marinho, D.: ‘Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach’, Comput. Biol. Med., 2010, 40, (2), pp. 124–137 (doi: 10.1016/j.compbiomed.2009.11.009).
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2017.0043
Related content
content/journals/10.1049/htl.2017.0043
pub_keyword,iet_inspecKeyword,pub_concept
6
6