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.

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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://www.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

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