Optic Disc Segmentation based on Template Matching and Active Contour Method

Abstract

This paper proposes a hybrid method that is capable of automatically implementing the Optic Disc (OD) segmentation. In the hybrid method, two steps were performed. First, the location of the OD was determined by template matching. Second, the OD location coordinates obtained in the first stage were given as inputs to the Active Contour Method applied to complete the OD segmentation. Furthermore, as part of this study,  Android based a program was developed to allow physicians the ability to independently access the proposed hybrid method results from anywhere and to add comments. Thus, the physician would be able to instantly track the patient. Performance evaluation of the proposed hybrid method was done separately for both localization and segmentation. The success of localization was confirmed on the basis of whether the determined coordinates corresponded to the OD. The segmentation process was assessed according to the parameters, as derived from a confusion matrix. The average Dice coefficient obtained for all images was 0.943, while the average values of accuracy, specificity and sensitivity parameters for all images were calculated as 0.90, 0.961 and 0.931, respectively. The final results obtained from the proposed hybrid method were checked by a physician, who observed that the OD was successfully segmented.

Keywords

• Optic Disc,Active Contour,Template Matching

References

1. [1] Z. Yavuz, C. Ikibas, U. Şevik, C. Köse, “Retinal Görüntülerde Optik Diskin Otomatik Olarak Çıkartılması İçin Bir Yöntem”, 5th International Advanced Technologies Symposium, Karabük, Türkiye, 13-15 Mayıs 2009, pp.8.
2. [2] J. Kaur, H.P. Sinha, “Automated localisation of optic disc and macula from fundus images”, International Journal of Advanced Research in Computer Science and Software Engineering, 2(4), 2012, pp.242-249.
3. [3] C. İkibaş, 1Retinal İmgelerde Optik Disk ve Makulanın Tespiti ve Değerlendirilmesi”, PhD thesis, Karadeniz Teknik Üniversitesi, 2012.
4. [4] H. Li, O. Chutatape, “Automatic location of optic disc in retinal images”, International Conference on Image Processing, Thessaloniki, Greece, 2001, pp. 837– 840.
5. [5] A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, “Colour Morphology and Snakes for Optic Disc Localisation”, 16th IEEE Int. Conf. Pattern Recognition, vol.1, 2002, pp. 743–746.
6. [6] A. Ahmed, B. Ritambhar, R. Kaamran, L. Vasudevan, “'Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey”, Journal of Ophthalmology, Article ID 180972, 2015, pp.28.
7. [7] S. Morales, V. Naranjo J. Angulo M. Alcañiz, “Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology”, IEEE Transactions on Medical Imaging, 32(4), 2013, pp. 786-796.
8. [8] M. Lalonde, M. Beaulieu, L. Gagnon, “Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching”, Medical Imaging, IEEE Transactions, 20(11), 2001, pp. 1193 – 1200.

9. [9] T. Chaichana, S. Yoowattana, et al, “'Edge detection of the optic disc in retinal images based on identification of a round shape”, International Symposium Communications and Information Technologies, 2008, pp. 670 –674.
10. [10] S.A. Tuncer, T. Selçuk M. Parlak, A. Alkan, “'Hybrid approach optic disc segmentation for retinal images”, International Artificial Intelligence and Data Processing Symposium (IDAP), 2017, pp.1-6.
11. [11] N. Muangnak, P. Aimmanee, S. Makhanov, “Automatic optic disc detection in retinal images using hybrid vessel phase portrait analysis”,Med Biol Eng Comput., 56(4), 2018, pp.583-598.
12. [12] R. Fulonga L. Wei, Y. Jinzhua et.al, “Automatic optic disc localization and segmentation in retinal images by a line operator and level sets”, Technology and Health Care, 24(2), 2016, pp. S767-S776.
13. [13] A. Dehghani, H. A. Moghaddam, M. S. Moin, “Optic disc localization in retinal images using histogram matching”, EURASIP Journal on Image and Video Processing, 2012, 2012(19).
14. [14] S. Lu, “Accurate and efficient optic disc detection and segmentation by a circular transformation”, IEEE Transactions on Medical Imaging, 30(12), 2011, pp. 2126–2133.
15. [15] A. Li, Z. Niu, J. Cheng, et.al., “Learning supervised descent directions for optic disc segmentation”, Neurocomputing, 275, 2018, pp.350-357.
16. [16] B. Dai, X. Wu, W. Bu, “Optic disc segmentation based on variational model with multiple energies”, Pattern Recognition, 64, 2017, pp. 226-235.
17. [17] B. Dashtbozorg, A. Mendonça, M.A. Campilho, “Optic disc segmentation using the sliding band filter”, Computers in Biology and Medicine, 56, 2015,pp. 1-12.
18. [18] J. H. Tan, U.R. Acharya, S.V. Bhandary et. al. “Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network”, Journal of Computational Science, 20, 2017, pp.70-79.
19. [19] L.A. Muhammed, “Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm”, International Journal of Biomedical Imaging, Article ID 2815163, 2018, pp.7.
20. [20] T.F. Chan, L.A. Vese, “Active contours without edges”, IEEE Transactions on Image Processing, 10(2), 2001.
21. [21] M. Kass, A.Witkin, D. Terzopoulos, “Snakes: active contour models”, International Journal of Computer Vision, 1, 1988, pp.321-331.
22. [22] P.P.R. Filho, P.C. Cortez, A.C. da S. Barros, et.al., “Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation A systematic evaluation on synthetic and real images”, Expert Systems with Applications, 41, 2014, pp.7707–7721.
23. [23] T.F. Chan, L.A. Vese, “Active contours without edges”, IEEE Transactions on Image Processing, 10(2), 2001.
24. [24] E. Isıkcı, D.G. Duru, “Multiple Skleroz Manyetik Rezonans Görüntülerinde Aktif Kontur Modeli ile Lezyon Tespiti”, Tıp Teknolojileri Ulusal Kongresi, Muğla,Türkiye, 2015.
25. [25] S.A. Tuncer, A. Alkan, “Segmentation of thyroid nodules with K-means algorithm on mobile devices”, 16th IEEE International Symposium on Computational Intelligence and Informatics, Budapest, Hungary, 2015, pp. 345-348.
26. [26] A. Alkan, S.A. Tuncer, M. Gunay, “Comparative MR image analysis for thyroid nodule detection and quantification”, Measurement, 47, 2014, pp. 861-868.
27. [27] M. Niemeijer, B. van Ginneken, F. Ter Haar et.al., “Automatic detection of the optic disc, fovea and vascular arch in digital color photographs of the retina”, Proceedings of the British Machine Vision Conference 2005, 109-118.