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Optic Disc and Cup Segmentation in Fundus Retinal Images Using Feature Detection and Morphological Techniques
Segmentation of optic disc and cup from the fundus retinal image helps in the detection of glaucoma which is one of the important causes for vision loss. In this study, we propose a method for optic disc followed by optical cup segmentation. The retinal input image is first preprocessed by applying histogram equalization and the connected components in the image are obtained. On applying the circle detection Hough transform, the circular-shaped optic disc is segmented from the other connected components. The morphological closing operation is applied on the segmented optic disc area to identify the optic cup location. The optic cup is then segmented by applying Otsu’s thresholding only on the extracted green channel of the retinal image. The proposed method was evaluated using DRISHTI-GS dataset containing 50 test images. The performance metrics such as dice coefficient, average boundary localization error and cup–disc ratio error were obtained by comparing the proposed method with the ground truth values of 51 training images in the DRISHTI-GS dataset.
Keywords
Feature Detection, Fundus Retinal Image, Morphological Closing, Optic Disc and Cup, Segmentation.
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- Jain, A. B., Prakash, V. J. and Bhende, M., Techniques of fundus imaging. Med. Vis. Res. Foundations, 2015, 33(2), 100.
- Gonzalez, R. C. and Woods, R. E., Image processing. In Digital Image Processing, Prentice Hall, New Jersey, USA, 2007, pp. 104–168.
- Youssif, Aliaa Abdel-Haleim Abdel-Razik, Atef Zaki Ghalwash, and Amr Ahmed Sabry Abdel-Rahman Ghoneim, Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter. IEEE Trans. Med. Imaging, 2008, 27(1), 11–18.
- Aquino, A., Gegúndez-Arias, M. E. and Marín, 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), 1860–1869.
- Welfer, D., Scharcanski, J., Kitamura, C. M., Dal Pizzol, M. M., Ludwig, L. W. and Marinho, D. R., Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput. Biol. Med., 2010, 40(2), 124–137.
- Abdullah, M., Fraz, M. M. and Barman, S. A., Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. Peer J, 2016, 4, e2003.
- Dehghani, A., Moghaddam, H. A. and Moin, M. S., Optic disc localization in retinal images using histogram matching. EURASIP J. Image Video Process., 2012, 2012, 11, 19.
- Ingle, R. and Mishra, P., Cup segmentation by gradient method for the assessment of glaucoma from retinal image. Int. J. Eng. Trends Technol., 2013, 4(6), 2540–2543.
- Muramatsu, C., Nakagawa, T., Sawada, A., Hatanaka, Y., Yamamoto, T. and Fujita, H., Automated determination of cup-to-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images. J. Biomed. Opt., 2011, 16(9), 096009-1– 096009-7.
- Yin, F., Liu, J., Wong, D. W. K., Tan, N. M., Cheung, C., Baskaran, M. and Wong, T. Y., Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis. In 25th IEEE International Symposium on Computer-based Medical Systems, 2012, pp. 1–6.
- Li, H. and Chutatape, O., Boundary detection of optic disk by a modified ASM method. Pattern Recogn., 2003, 36(9), 2093– 2104.
- Cheng, J. et al., Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging, 2013, 32(6), 1019–1032.
- Tan, N. M., Xu, Y., Goh, W. B. and Liu, J., Robust multi-scale superpixel classification for optic cup localization. Comput. Med. Imaging Graphics, 2015, 40, 182–193.
- Almazroa, A. et al., Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J. Ophthalmol., 2015, 180972-1–180972-28.
- Mathworks, I., MATLAB: R2014a. Mathworks Inc, Natick, MA, USA, 2014.
- Sivaswamy, J., Krishnadas, S. R., Joshi, G. D., Jain, M. and Tabish, A. U. S., DRISHTI-GS: retinal image dataset for optic nerve head (onh) segmentation. In 11th IEEE International Symposium on Biomedical Imaging, April 2014, pp. 53–56.
- Dice, L. R., Measures of the amount of ecologic association between species. Ecology, 1945, 26(3), 297–302.
- Taha, A. A. and Hanbury, A., Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging, 2015, 15(1), 29.
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