An Intelligent Mathematically Modified Fuzzy C-Means Clustering Technique for Fundus Image Segmentation for Diabetic Retinopathy Identification

Authors

  • Anamika Raj Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia and Department of Computer Science, Applied College Al Mahala, 61421, King Khalid University, Saudi Arabia
  • Noor Maizura Mohamad Noor Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Rosmayati Mohemad Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Noor Azliza Che Mata Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Shahid Hussain Department of Computer Science, Applied College Al Mahala, 61421, King Khalid University, Saudi Arabia

Keywords:

Diabetes, segmentation, diabetic retinopathy, neural network, convolution, feature fusion, lesion, vessel

Abstract

Diabetic Retinopathy (DR) is a significant threat to individuals with diabetes, resulting in retinal damage that can lead to vision loss. Early and accurate detection of DR is essential for effective therapy and vision preservation. This study is motivated by a broad range of goals designed to advance the study of diabetic retinopathy (DR) analysis using cutting-edge image processing methods. Firstly, it seeks to enhance pre-processing methods, including techniques like Gabor filtering and Gaussian filtering, with the goal of elevating the quality of fundus images by reducing noise, enhancing features, and preparing them for subsequent analysis. Secondly, the core focus lies in the development and fine-tuning of segmentation algorithms, particularly Mathematically Modified Fuzzy C-Means Clustering (MMFCM), for precise identification of DR-related lesions, such as microaneurysms (MA), haemorrhages (HE), exudates (EX), and Intraretinal haemorrhages (IH) within retinal images. Thirdly, the research aims to establish robust quantitative metrics, including Matthews Correlation Coefficient (MCC), Dice coefficient (DICE), and Intersection-over-Union (IoU), to rigorously assess the accuracy and quality of segmentation results. The incorporation of MMFCM improves the segmentation and analysis of retinal pictures, allowing medical personnel to identify DR early and implement timely therapies, protecting patients' vision and raising their overall quality of life.

Downloads

Download data is not yet available.

References

Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., & Simó, R. (2020). Screening for diabetic retinopathy: new perspectives and challenges. The Lancet Diabetes & Endocrinology, 8(4), 337-347.

Teo, Z. L., Tham, Y. C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., ... & Cheng, C. Y. (2021). Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology, 128(11), 1580-1591.

Antonetti, D. A., Silva, P. S., & Stitt, A. W. (2021). Current understanding of the molecular and cellular pathology of diabetic retinopathy. Nature Reviews Endocrinology, 17(4), 195-206.

Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U., ... & Jadoon, W. (2019). A deep learning ensemble approach for diabetic retinopathy detection. Ieee Access, 7, 150530-150539.

Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., ... & Jia, W. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature communications, 12(1), 3242.

Levin, L. A., Sengupta, M., Balcer, L. J., Kupersmith, M. J., & Miller, N. R. (2021). Report From the National Eye Institute Workshop on Neuro-Ophthalmic Disease Clinical Trial Endpoints: Optic Neuropathies. Investigative ophthalmology & visual science, 62(14), 30-30.

Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2020). Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing, 1-14.

Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S., Abramoff, M., & Ting, D. S. (2020). Artificial intelligence for diabetic retinopathy screening: a review. Eye, 34(3), 451-460.

Forrester, J. V., Kuffova, L., & Delibegovic, M. (2020). The role of inflammation in diabetic retinopathy. Frontiers in immunology, 11, 583687.

Eswari, M. S., & Balamurali, S. (2022). An Optimal Diabetic Features-Based Intelligent System to Predict Diabetic Retinal Disease. In Computational Intelligence and Data Sciences (pp. 91- 106). CRC Press.

Simó-Servat, O., Hernández, C., & Simó, R. (2019). Diabetic retinopathy in the context of patients with diabetes. Ophthalmic research, 62(4), 211-217.

Skouta, A., Elmoufidi, A., Jai-Andaloussi, S., & Ouchetto, O. (2022). Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network. Journal of Big Data, 9(1), 1-24.

Reddy, S. S., Sethi, N., & Rajender, R. (2021). Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods. EAI Endorsed Transactions on Scalable Information Systems, 8(29), e1.

Shen, Z., Wu, Q., Wang, Z., Chen, G., & Lin, B. (2021). Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data. Sensors, 21(11), 3663.

Sambyal, N., Saini, P., Syal, R., & Gupta, V. : Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybernetics and Biomedical Engineering, 40(3), 1094-1109,(2020)

Keerthiveena, B., Veerakumar, T., Esakkirajan, S., & Subudhi, B. N. (2019, December). Computer-aided diagnosis for diabetic retinopathy based on firefly algorithm. In 2019 11th International Conference on Advanced Computing (ICoAC) (pp. 310-315). IEEE.

Erciyas, A., & Barışçı, N. (2021). An effective method for detecting and classifying diabetic retinopathy lesions based on deep learning. Computational and Mathematical Methods in Medicine, 2021, 1-13.

Saini, M., & Susan, S. (2022). Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets. Computers in Biology and Medicine, 149, 105989.

Downloads

Published

05.12.2023

How to Cite

Raj, A. ., Mohamad Noor , N. M. ., Mohemad , R. ., Mata, N. A. C. ., & Hussain, S. . (2023). An Intelligent Mathematically Modified Fuzzy C-Means Clustering Technique for Fundus Image Segmentation for Diabetic Retinopathy Identification . International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 603–612. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4180

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Copyright (c) 2023 Anamika Raj, Noor Maizura Mohamad Noor , Rosmayati Mohemad , Noor Azliza Che Mata, Shahid Hussain

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.