Proceedings of the Multimedia University Engineering Conference (MECON 2022)

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Segmentation of Diabetic Retinopathy Based on Retinal Fundus Images Using Thresholding Technique

Authors
Nur Hasanah Ali1, *, Nur Asyiqin Amir Hamzah1, Norhashimah Mohd Saad2, Rania Mahfooz3, Abdul Rahim Abdullah4
1Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
2Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
3Oriental Melaka Straits Medical Centre, Melaka, Malaysia
4Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
*Corresponding author. Email: hasanah.ali@mmu.edu.my
Corresponding Author
Nur Hasanah Ali
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-082-4_17How to use a DOI?
Keywords
Diabetic retinopathy; segmentation; thresholding; retina fundus image
Abstract

Diabetic Retinopathy is damage to the retina caused by complications of diabetes mellitus. Exudates are the primary sign of Diabetic Retinopathy identified on the opthalmoscope as white or yellowish areas with varying sizes, shapes and locations in the retina. Early detection can potentially reduce the risk of blindness. Diabetic retinopathy diagnosis for early-stage detection used a manual or semi-automated system that only an ophthalmologist can do it. This project proposed an automatic analysis of diabetic retinopathy and lesion of the eye in the retinal region using image processing techniques. The proposed technique is using thresholding technique. The data is taken from the DIRECTDB1 database. Pre-processing is applied first and then the image is segmented into 8 x 8 regions. An image histogram is then been calculated at each region to look for the maximum number of pixels for each intensity level. By comparing normal and exudate regions, the best threshold is found. The proposed technique is validated based on the accuracy, specificity, and sensitivity of exudate detection from the ground truth image reference. The mean from the range of the abnormal region of interest (ROI) is also calculated whether it is within range of the optimal value. In conclusion, adaptive thresholding is the method of choice to develop an automated detection of diabetic retinopathy. The result shows an accuracy of 65.79%, sensitivity is 31.58%, and specificity is 100%. The best segmentation and classification performance is achieved in the range of abnormal region ROI which is 0.35 to 0.55.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Series
Advances in Engineering Research
Publication Date
23 December 2022
ISBN
10.2991/978-94-6463-082-4_17
ISSN
2352-5401
DOI
10.2991/978-94-6463-082-4_17How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nur Hasanah Ali
AU  - Nur Asyiqin Amir Hamzah
AU  - Norhashimah Mohd Saad
AU  - Rania Mahfooz
AU  - Abdul Rahim Abdullah
PY  - 2022
DA  - 2022/12/23
TI  - Segmentation of Diabetic Retinopathy Based on Retinal Fundus Images Using Thresholding Technique
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 164
EP  - 173
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_17
DO  - 10.2991/978-94-6463-082-4_17
ID  - Ali2022
ER  -