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Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space

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Abstract

Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.

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References

  1. Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Automated identification of diabetic retinal exudates in digital colour images. Br. J. Ophthalmol. 87:1220–1223, 2003. https://doi.org/10.1136/bjo.87.10.1220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pathan, S., Kumar, P., Pai, R., Bhandary, S. V.: Automated detection of optic disc contours in fundus images using decision tree classifier. Biocybern. Biomed. Eng. 40:52–64, 2020. https://doi.org/10.1016/j.bbe.2019.11.003

    Article  Google Scholar 

  3. Kumar, S., Adarsh, A., Kumar, B., Singh, A.K.: An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation. Opt. Laser Technol. 121, 2020. https://doi.org/10.1016/j.optlastec.2019.105815

  4. Uribe-Valencia, L.J., Martínez-Carballido, J.F.: Automated Optic Disc region location from fundus images: Using local multi-level thresholding, best channel selection, and an Intensity Profile Model. Biomed. Signal Process. Control. 51:148–161, 2019. https://doi.org/10.1016/j.bspc.2019.02.006

    Article  Google Scholar 

  5. Reza, M.N.: Automatic detection of optic disc in color fundus retinal images using circle operator. Biomed. Signal Process. Control. 45: 274–283, 2018. https://doi.org/10.1016/j.bspc.2018.05.027

    Article  Google Scholar 

  6. Thakur, N., Juneja, M.: Optic disc and optic cup segmentation from retinal images using hybrid approach. Expert Syst. Appl. 127: 308–322, 2019. https://doi.org/10.1016/j.eswa.2019.03.009

    Article  Google Scholar 

  7. Gui, B., Shuai, R.J., Chen, P.: Optic disc localization algorithm based on improved corner detection. Procedia Comput. Sci. 131:311–319, 2018. https://doi.org/10.1016/j.procs.2018.04.169

    Article  Google Scholar 

  8. Dehghani, A., Moghaddam, H.A., Moin, M.S.: Optic disc localization in retinal images using histogram matching. Eurasip J. Image Video Process. 2012. https://doi.org/10.1186/1687-5281-2012-19

    Article  Google Scholar 

  9. Pourreza-Shahri, R., Tavakoli, M., Kehtarnavaz, N.: Computationally efficient optic nerve head detection in retinal fundus images. Biomed. Signal Process. Control. 11:63–73, 2014. https://doi.org/10.1016/j.bspc.2014.02.011

    Article  Google Scholar 

  10. Harangi, B., Hajdu, A.: Detection of the optic disc in fundus images by combining probability models. Comput. Biol. Med. 65: 10–24 , 2015. https://doi.org/10.1016/j.compbiomed.2015.07.002

    Article  PubMed  Google Scholar 

  11. Wang, C., Kaba, D., Li, Y.: Level Set Segmentation of Optic Discs from Retinal Images. J. Med. Bioeng. 4: 213–220, 2015. https://doi.org/10.12720/jomb.4.3.213-220

  12. Ahmed, M.I., Amin, M.A.: High speed detection of optical disc in retinal fundus image. Signal, Image Video Process. 9: 77–85 ,2015. https://doi.org/10.1007/s11760-012-0412-3

    Article  Google Scholar 

  13. Dashtbozorg, B., Mendonça, A.M., Campilho, A.: Optic disc segmentation using the sliding band filter. Comput. Biol. Med. 56: 1–12, 2015. https://doi.org/10.1016/j.compbiomed.2014.10.009

    Article  PubMed  Google Scholar 

  14. Mary, M.C.V.S., Rajsingh, E.B., Jacob, J.K.K., Anandhi, D., Amato, U., Selvan, S.E.: An empirical study on optic disc segmentation using an active contour model. Biomed. Signal Process. Control. 18: 19–29, 2015. https://doi.org/10.1016/j.bspc.2014.11.003

    Article  Google Scholar 

  15. Bharkad, S.: Automatic segmentation of optic disk in retinal images. Biomed. Signal Process. Control. 31: 483–498, 2017. https://doi.org/10.1016/j.bspc.2016.09.009

    Article  Google Scholar 

  16. Kamble, R., Kokare, M., Deshmukh, G., Hussin, F.A., Mériaudeau, F.: Localization of optic disc and fovea in retinal images using intensity based line scanning analysis. Comput. Biol. Med. 87: 382–396, 2017. https://doi.org/10.1016/j.compbiomed.2017.04.016

    Article  PubMed  Google Scholar 

  17. Zhou, W., Yi, Y., Gao, Y., Dai, J.: Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior. Comput. Math. Methods Med. 2019. https://doi.org/10.1155/2019/8973287

    Article  PubMed  PubMed Central  Google Scholar 

  18. Naqvi, S.S., Fatima, N., Khan, T.M., Rehman, Z.U., Khan, M.A.: Automatic optic disk detection and segmentation by variational active contour estimation in retinal fundus images. Signal, Image Video Process. 13:1191–1198, 2019. https://doi.org/10.1007/s11760-019-01463-y

    Article  Google Scholar 

  19. Yu, H., Barriga, E.S., Agurto, C., Echegaray, S., Pattichis, M.S., Bauman, W., Soliz, P.: Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans. Inf. Technol. Biomed. 16:644–657, 2012. https://doi.org/10.1109/TITB.2012.2198668

    Article  CAS  PubMed  Google Scholar 

  20. Tan, J.H., Acharya, U.R., Bhandary, S. V., Chua, K.C., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20: 70–79, 2017. https://doi.org/10.1016/j.jocs.2017.02.006

    Article  Google Scholar 

  21. Yu, S., Xiao, D., Frost, S., Kanagasingam, Y.: Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput. Med. Imaging Graph. 74: 61–71, 2019. https://doi.org/10.1016/j.compmedimag.2019.02.005

    Article  PubMed  Google Scholar 

  22. Liu, S., Hong, J., Lu, X., Jia, X., Lin, Z., Zhou, Y., Liu, Y., Zhang, H.: Joint optic disc and cup segmentation using semi-supervised conditional GANs. Comput. Biol. Med. 115, 2019. https://doi.org/10.1016/j.compbiomed.2019.103485

  23. Lim, G., Cheng, Y., Hsu, W., Lee, M.L.: Integrated optic disc and cup segmentation with deep learning. Proc. - Int. Conf. Tools with Artif. Intell. ICTAI. 2016-Janua, 162–169, 2016. https://doi.org/10.1109/ICTAI.2015.36

  24. Jana, S., Parekh, R., Sarkar, B.: A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image. Handb. Comput. Intell. Biomed. Eng. Healthc. 65–91, 2021. https://doi.org/10.1016/b978-0-12-822260-7.00012-1

  25. Tulsani, A., Kumar, P., Pathan, S.: Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture. Biocybern. Biomed. Eng. 41: 819–832, 2021. https://doi.org/10.1016/j.bbe.2021.05.011

    Article  Google Scholar 

  26. Veena, H.N., Muruganandham, A., Senthil Kumaran, T.: A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J. King Saud Univ. - Comput. Inf. Sci. 2021. https://doi.org/10.1016/j.jksuci.2021.02.003

  27. Sengupta, S., Singh, A., Leopold, H.A., Gulati, T., Lakshminarayanan, V.: Ophthalmic diagnosis using deep learning with fundus images – A critical review. Artif. Intell. Med. 102, 2020. https://doi.org/10.1016/j.artmed.2019.101758

  28. GeethaRamani, R., Balasubramanian, L.: Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening. Comput. Methods Programs Biomed. 160: 153–163, 2018. https://doi.org/10.1016/j.cmpb.2018.03.020

    Article  Google Scholar 

  29. Joshi, S., Karule, P.T.: A review on exudates detection methods for diabetic retinopathy. Biomed. Pharmacother. 97: 1454–1460, 2018. https://doi.org/10.1016/j.biopha.2017.11.009

    Article  CAS  PubMed  Google Scholar 

  30. Pereira, C., Veiga, D., Mahdjoub, J., Guessoum, Z., Gonçalves, L., Ferreira, M., Monteiro, J.: Using a multi-agent system approach for microaneurysm detection in fundus images. Artif. Intell. Med. 60: 179–188, 2014. https://doi.org/10.1016/j.artmed.2013.12.005

    Article  PubMed  Google Scholar 

  31. Wu, J., Zhang, S., Xiao, Z., Zhang, F., Geng, L., Lou, S., Liu, M.: Hemorrhage detection in fundus image based on 2D Gaussian fitting and human visual characteristics. Opt. Laser Technol. 110: 69–77, 2019. https://doi.org/10.1016/j.optlastec.2018.07.049

    Article  Google Scholar 

  32. Umesawa, M., Kitamura, A., Kiyama, M., Okada, T., Imano, H., Ohira, T., Yamagishi, K., Saito, I., Iso, H.: Relationship between HbA1c and risk of retinal hemorrhage in the Japanese general population: The Circulatory Risk in Communities Study (CIRCS). J. Diabetes Complications. 30: 834–838, 2016. https://doi.org/10.1016/j.jdiacomp.2016.03.023

    Article  PubMed  Google Scholar 

  33. Savino, P., Wall, M.: Optic disk edema with cotton-wool spots. Surv. Ophthalmol. 39: 502–508, 1995. https://doi.org/10.1016/S0039-6257(05)80057-8

    Article  Google Scholar 

  34. Hagiwara, Y., Koh, J.E.W., Tan, J.H., Bhandary, S. V., Laude, A., Ciaccio, E.J., Tong, L., Acharya, U.R.: Computer-aided diagnosis of glaucoma using fundus images: A review. Comput. Methods Programs Biomed. 165: 1–12 , 2018. https://doi.org/10.1016/j.cmpb.2018.07.012

    Article  PubMed  Google Scholar 

  35. Park, M., Jin, J.S., Luo, S.: Locating the optic disc in retinal images. Proc. - Comput. Graph. Imaging Vis. Tech. Appl. CGIV’06. 141–145, 2006. https://doi.org/10.1109/CGIV.2006.63

  36. Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordóñez-Varela, J.R., Massin, P., Erginay, A., Charton, B., Klein, J.C.: Feedback on a publicly distributed image database: The Messidor database. Image Anal. Stereol. 33: 231–234, 2014. https://doi.org/10.5566/ias.1155

    Article  Google Scholar 

  37. Carmona, E.J., Rincón, M., García-Feijoó, J., Martínez-de-la-Casa, J.M.: Identification of the optic nerve head with genetic algorithms. Artif. Intell. Med. 43: 243–259, 2008. https://doi.org/10.1016/j.artmed.2008.04.005

    Article  PubMed  Google Scholar 

  38. Karaboga, D.: An idea based on Honey Bee Swarm for Numerical Optimization. Tech. Rep. TR06, Erciyes Univ. 10 (2005)

  39. Aslan, S.: A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization. Memetic Comput. 12: 129–150, 2020. https://doi.org/10.1007/s12293-020-00298-2

    Article  Google Scholar 

  40. Toptaş, B., Hanbay, D.: A new artificial bee colony algorithm-based color space for fire/flame detection. Soft Comput. 24: 10481–10492, 2020. https://doi.org/10.1007/s00500-019-04557-4

    Article  Google Scholar 

  41. Khatami, A., Mirghasemi, S., Khosravi, A., Lim, C.P., Nahavandi, S.: A new PSO-based approach to fire flame detection using K-Medoids clustering. Expert Syst. Appl. 68: 69–80, 2017. https://doi.org/10.1016/j.eswa.2016.09.021

    Article  Google Scholar 

  42. Jebaseeli, T.J., Deva Durai, C.A., Peter, J.D.: Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM. Optik (Stuttg). 199: 2019. https://doi.org/10.1016/j.ijleo.2019.163328

  43. Hashemzadeh, M., Adlpour Azar, B.: Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods. Artif. Intell. Med. 95: 1–15, 2019. https://doi.org/10.1016/j.artmed.2019.03.001

    Article  PubMed  Google Scholar 

  44. Toman, H., Kovacs, L., Jonas, A., Hajdu, L., Hajdu, A.: Generalized weighted majority voting with an application to algorithms having spatial output. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 7209 LNAI, 56–67, 2012. https://doi.org/10.1007/978-3-642-28931-6_6

  45. Lupaşcu, C.A., Di Rosa, L., Tegolo, D.: Automated detection of optic disc location in retinal images. Proc. - IEEE Symp. Comput. Med. Syst. 17–22, 2008. https://doi.org/10.1109/CBMS.2008.15

  46. Rodrigues, L.C., Marengoni, M.: Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed. Signal Process. Control. 36: 39–49, 2017. https://doi.org/10.1016/j.bspc.2017.03.014

    Article  Google Scholar 

  47. Rangayyan, R.M., Zhu, X., Ayres, F.J., Ells, A.L.: Detection of the optic nerve head in fundus images of the retina with gabor filters and phase portrait analysis. J. Digit. Imaging. 23: 438–453, 2010. https://doi.org/10.1007/s10278-009-9261-1

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhu, X., Rangayyan, R.M., Ells, A.L.: Detection of the optic nerve head in fundus images of the retina using the hough transform for circles. J. Digit. Imaging. 23: 332–341 ,2010. https://doi.org/10.1007/s10278-009-9189-5

    Article  PubMed  Google Scholar 

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Funding

This study was funded by the Inonu university scientific research and coordination unit with the Project number FDK-2020–2109.

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Correspondence to Buket Toptaş.

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Toptaş, B., Toptaş, M. & Hanbay, D. Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space. J Digit Imaging 35, 302–319 (2022). https://doi.org/10.1007/s10278-021-00566-8

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