FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-090406

Research Article |

An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Author(s): Parul Datta*, Prasenjit Das and Abhishek Kumar

Published In : International Journal of Electrical and Electronics Research (IJEER)        volume 9, issue 4

Publisher : FOREX Publication

Published : 30 December 2021

e-ISSN : 2347-470X

Page(s) : 125-134

DOI: https://doi.org/10.37391/IJEER.090406

Abstract

The detection of eye illnesses requires a thorough inspection of all of the eye's structures. Most significantly, the presence of blood vessels, an optical disc, and any other unwelcome objects, if any are discovered, is critical in determining the type of eye disease present. Specifically, the goal of this research is to establish a thorough segmentation framework that will aid in the detection of anatomical anomalies in the eye. A novel segmentation technique for analyzing blood vessels, optical disc health, and the presence of exudates has been added into the software. - It was decided to add the detection of aberrant objects such as exudates into the algorithm in order to develop a generic segmentation method. In order to verify the accuracy of the segmentation at each stage, random sampling is employed at each stage. The segmentation is then validated using the intersection over union metric. The accuracy of the integrated segmentation method as a whole is 91.66 percent.

Keywords: Segmentation, fundus images, convolution filters, diabetic retinopathy, Otsu.




Parul Datta*, CUSET, Chitkara University, Himachal Pradesh, India; Email: parul.datta@chitkarauniversity.edu.in

Prasenjit Das, CUSCA,Chitkara University, Himachal Pradesh, India

Abhishek Kumar, CUSET, Chitkara University, Himachal Pradesh, India

    [1] S. M. Saleem, L. R. Pasquale, P. A. Sidoti, and J. C. Tsai, “Virtual Ophthalmology: Telemedicine in a COVID-19 Era,” Am. J. Ophthalmol., vol. 216, no. 12, pp. 237–242, 2020.
    [2] R. Sayeed, D. Gottlieb, and K. D. Mandl, “SMART Markers: collecting patient-generated health data as a standardized property of health information technology,” npj Digit. Med., vol. 3, no. 1, 2020, doi: 10.1038/s41746-020-0218-6.
    [3] M. Ponnibala, E. B. Priyanka, and S. Thangavel, “Proliferative Diabetic Retinopathy Diagnostic Investigation Using Retinal Blood Vessels Mining Technique,” Sens. Imaging, vol. 22, no. 1, 2021, doi: 10.1007/s11220-021-00331-9.
    [4] K. Mittal and V. M. A. Rajam, “Computerized retinal image analysis - a survey,” Multimed. Tools Appl., vol. 79, no. 31–32, pp. 22389–22421, 2020, doi: 10.1007/s11042-020-09041-y.
    [5] F. Abdullah et al., “A Review on Glaucoma Disease Detection Using Computerized Techniques,” IEEE Access, vol. 9. pp. 37311–37333, 2021, doi: 10.1109/ACCESS.2021.3061451.
    [6] A. M. Syed, M. U. Akbar, and J. Fatima, “An overview of oct techniques for detection of ophthalmic syndromes,” in EAI/Springer Innovations in Communication and Computing, 2019, pp. 109–116.
    [7] M. H. Sarhan et al., “Machine Learning Techniques for Ophthalmic Data Processing: A Review,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 12, pp. 3338–3350, 2020, doi: 10.1109/JBHI.2020.3012134.
    [8] E. Y. K. Ng, U. R. Acharya, A. Campilho, and J. S. Suri, Image analysis and modeling in ophthalmology. 2014.
    [9] R. Bernardes, P. Serranho, and C. Lobo, “Digital ocular fundus imaging: A review,” Ophthalmologica, vol. 226, no. 4.pp. 161-181,2011, doi: 10.1159/000329597.
    [10] G. Lim, V. Bellemo, Y. Xie, X. Q. Lee, M. Y. T. Yip, and D. S. W. Ting, “Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review,” Eye Vis., 2020, doi: 10.1186/s40662-020-00182-7.
    [11] S. Jeon, Y. Liu, J. P. O. Li, D. Webster, L. Peng, and D. Ting, “AI papers in ophthalmology made simple,” Eye (Basingstoke), vol. 34, no. 11. pp. 1947–1949, 2020, doi: 10.1038/s41433-020-0929-6.
    [12] D. Lokuarachchi, L. Muthumal, K. Gunarathna, and T. D. Gamage, “Detection of Red Lesions in Retinal Images Using Image Processing and Machine Learning Techniques,” in MERCon 2019 - Proceedings, 5th International Multidisciplinary Moratuwa Engineering Research Conference, 2019, pp. 550–555, doi: 10.1109/MERCon.2019.8818794.
    [13] S. Long, J. Chen, A. Hu, H. Liu, Z. Chen, and D. Zheng, “Microaneurysms detection in color fundus images using machine learning based on directional local contrast,” doi: 10.1186/s12938-020-00766-3.
    [14] D. S. Sisodia, S. Nair, and P. Khobragade, “Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of Diabetic Retinopathy,” Biomed. Pharmacol. J., 2017, doi: 10.13005/bpj/1148.
    [15] S. S. Johnson, J. K. Wang, M. S. Islam, M. J. Thurtell, R. H. Kardon, and M. K. Garvin, “Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11039 LNCS, pp. 277–284, doi: 10.1007/978-3-030-00949-6_33.
    [16] S. Morales, V. Naranjo, J. Angulo, A. G. Legaz-Aparicio, and R. Verdú-Monedero, “Retinal network characterization through fundus image processing: Significant point identification on vessel centerline,” Signal Process. Image Commun., vol. 59, 2017, doi: 10.1016/j.image.2017.03.013.
    [17] T. A. Soomro, A. M. Bughio, S. H. Siyal, A. A. Panwar, and N. Nizamani, “A Wide-Ranging Review on Diabetic Retinotherapy,” Quaid-e-Awam Univ. Res. J. Eng. Sci. Technol., vol. 18, no. 02, 2020, doi: 10.52584/qrj.1802.25.
    [18] P. Powar and P. C. R. Jadhav, “Comparative Study of Classifiers for Diagnosis of Microaneurysm,” 2016.
    [19] C. Méjécase, S. Malka, Z. Guan, A. Slater, G. Arno, and M. Moosajee, “Practical guide to genetic screening for inherited eye diseases,” Ther. Adv. Ophthalmol., vol. 12, 2020, doi: 10.1177/2515841420954592.
    [20] T. Ratanapakorn, A. Daengphoonphol, N. Eua-Anant, and Y. Yospaiboon, “Digital image processing software for diagnosing diabetic retinopathy from fundus photograph,” Clin. Ophthalmol., vol. 13, pp. 641–668, 2019, doi: 10.2147/OPTH.S195617.
    [21] B. Bataineh and K. H. Almotairi, “Enhancement Method for Color Retinal Fundus Images Based on Structural Details and Illumination Improvements,” Arab. J. Sci. Eng., 2021, doi: 10.1007/s13369-021-05429-6.
    [22] K. Kipli et al., “A Review on the Extraction of Quantitative Retinal Microvascular Image Feature,” Computational and Mathematical Methods in Medicine. 2018, doi: 10.1155/2018/4019538.
    [23] Y. Elloumi, M. Akil, and N. Kehtarnavaz, “A Computationally Efficient Retina Detection and Enhancement Image Processing Pipeline for Smartphone-Captured Fundus Images A Computationally Efficient Retina Detection and Enhancement Image Processing Pipeline for Smartphone-Captured Fundus,” Images. J. Multimed. Inf. Syst., vol. 0, no. 0, pp. 1–10, 2018, [Online]. Available: http://dx.doi.org/00.0000/JMIS.0000.00.0.000.
    [24] Z. Shen, H. Fu, J. Shen, and L. Shao, “Modeling and Enhancing Low-Quality Retinal Fundus Images,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 996–1006, 2021, doi: 10.1109/TMI.2020.3043495.
    [25] G. J. Anitha and K. G. Maria, “Detecting Hard Exudates in Retinal Fundus Images Using Convolutinal Neural Networks,” 2018, doi: 10.1109/ICCTCT.2018.8551079.
    [26] C. Bhardwaj, S. Jain, and M. Sood, “Hierarchical severity grade classification of non-proliferative diabetic retinopathy,” J. Ambient Intell. Humaniz. Comput., 2020, doi: 10.1007/s12652-020-02426-9.
    [27] S. S. Chowhan, R. S. Deore, and S. A. Naik, “Retinal Vessel Segmentation of Non-Proliferative Diabetic Retinopathy,” Int. J. Appl. Evol. Comput., 2018, doi: 10.4018/ijaec.2019010102.
    [28] M. S. Mabrouk, N. H. Solouma, and Y. M. Kadah, “Survey of Retinal Image Segmentation and Registration,” GVIP J., vol. 6, no. 2, p. 1, 2006.
    [29] M. M. Fraz et al., “Blood vessel segmentation methodologies in retinal images - A survey,” Comput. Methods Programs Biomed., vol. 108, no. 1, pp. 407–433, 2012, doi: 10.1016/j.cmpb.2012.03.009.
    [30] C. Zhu et al., “A survey of retinal vessel segmentation in fundus images,” Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal Comput. Des. Comput. Graph, vol. 27, no. 11, pp. 2046–2057, 2015.
    [31] P. Powar and C. R., “A Survey of Microaneurysms Detection using Segmentation Techniques in Fundus Images,” Int. J. Comput. Appl., vol. 135, no. 1, pp. 32–34, 2016, doi: 10.5120/ijca2016908311.
    [32] A. Raj, A. K. Tiwari, and M. G. Martini, “Fundus image quality assessment: Survey, challenges, and future scope,” IET Image Process., vol. 13, no. 8, pp. 1211–1224, 2019, doi: 10.1049/iet-ipr.2018.6212.
    [33] S. Joshi and P. T. Karule, “Mathematical morphology for microaneurysm detection in fundus images,” Eur. J. Ophthalmol., vol. 30, no. 5, pp. 1135–1142, 2020, doi: 10.1177/1120672119843021.
    [34] R. K. Singh and R. Gorantla, “DMenet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs,” PLoS One, 2020, doi: 10.1371/journal.pone.0220677.
    [35] K. Lin, Li; Li, Meng; Huang, Yijin; Cheng, Pujin; Xia, Honghui; Wang, “The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading.” 2020.
    [36] J. B. Jonas, W. M. Budde, and S. Panda-Jonas, “Ophthalmoscopic evaluation of the optic nerve head,” Survey of Ophthalmology, vol. 43, no. 4. Elsevier Inc., pp. 293–320, 1999, doi: 10.1016/S0039-6257(98)00049-6.
    [37] https://learning-center.homesciencetools.com/article/eye-and-vision/
    [38] https://medium.com/@gautamkumarjaiswal/retina-blood-vessel-segmentation-using-matlab-ce5cfd1fa974.
    [39] Bagadam David (2021). Localization of Hard Exudates in RetinalFundus Images (https://www.mathworks.com/matlabcentral/fileexchange/69542-localization-of-hard-exudates-in-retinal-fundus-images), MATLAB Central File Exchange. Retrieved December 14, 2021.

Parul Datta, Prasenjit Das and Abhishek Kumar (2021), An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments. IJEER 9(4), 125-134. DOI: 10.37391/IJEER.090406.