Optic Disk Segmentation Using Histogram Analysis
Keywords:
Optic Disk, Segmentation, Histogram Analysis
Abstract
In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database
References
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[3] M. N. Reza, “Automatic detection of optic-disc in color fundus retinal images using circle operator,” Biomed. Signal Process. Control, vol. 45, pp. 274–283, 2018.
[4] Z. Fan et al., “Optic Disk Detection in Fundus Image Based on Structured Learning,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 1, pp. 224–234, 2018.
[5] L. Y. Xue, J. W. Lin, X. R. Cao, S. H. Zheng, and L. Yu, “Optic disk detection and segmentation for retinal images using saliency model based on clustering,” J. Comput., vol. 29, no. 5, pp. 66–79, 2018.
[6] A. Budai, A. Aichert, B. Vymazal, J. Hornegger, and G. Michelson, “Optic disk localization using fast radial symmetry transform,” Proc. - IEEE Symp. Comput. Med. Syst., pp. 59–64, 2013.
[7] M. Usman Akram, A. Khan, K. Iqbal, and W. H. Butt, “Retinal images: Optic disk localization and detection,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6112 LNCS, no. PART 2, pp. 40–49, 2010.
[8] J. Pinão and C. M. Oliveira, “Fovea and optic disc detection in retinal images with visible lesions,” IFIP Adv. Inf. Commun. Technol., vol. 372 AICT, pp. 543–552, 2012.
[9] H. Yu et al., “Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 644–657, 2012.
[10] G. D. Joshi, J. Sivaswamy, and S. R. Krishnadas, “Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment,” IEEE Trans. Med. Imaging, vol. 30, no. 6, pp. 1192–1205, 2011.
[11] A. H. Al-Sharfaa, A. Y. Yousif, and E. H. Al-Saadi, “Localization of Optic Disk and Exudates Detection in Retinal Fundus Images,” J. Phys. Conf. Ser., vol. 1804, no. 1, 2021.
[12] M. Esmaeili, H. Rabbani, A. M. Dehnavi, and A. Dehghani, “Automatic detection of exudates and optic disk in retinal images using curvelet transform,” IET Image Process., vol. 6, no. 7, pp. 1005–1013, 2012.
[13] H. M. Ünver, Y. Kökver, E. Duman, and O. A. Erdem, “Statistical edge detection and circular hough transform for Optic disk localization,” Appl. Sci., vol. 9, no. 2, pp. 1–16, 2019.
[14] J. Titus and S. Geroge, “A Comparison Study On Different Interpolation Methods Based On Satellite Images,” Int. J. Eng. Res. Technol., vol. 2, no. 6, pp. 82–85, 2013.
[15] S. Yuan, M. Abe, A. Taguchi, and M. Kawamata, “High accuracy bicubic interpolation using image local features,” IEICE Trans. Fundam. Electron. Commun. Comput. Sci., vol. E90-A, no. 8, pp. 1611–1615, 2007.
[16] A. Bhuiyan, R. Kawasaki, T. Wong, and R. Kotagiri, “A New and Efficient Method for Automatic Optic Disc Detection Using Geometrical Features,” in World Congress on Medical Physics and Biomedical Engineering, 2009, vol. 25, no. 4, pp. 2295–2298.
[17] J. Cohen, “Approximate power and sample size determination for common one-sample and two-sample hypothesis tests,” Educ. Psychol. Meas., vol. 30, no. 4, pp. 811–831, 1970.
Published
2022-03-02
How to Cite
[1]
B. Triwijoyo, “Optic Disk Segmentation Using Histogram Analysis”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 1, no. 1, pp. 27-34, Mar. 2022.
Section
Articles
