International Journal Of Engineering Technology And Management Sciences

IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT & OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING

AUTHOR(S)

T M SOUJANYA, K PRASAD BABU

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.022

ABSTRACT
In both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. Unlike typical foreground and background segmentation in normal image processing, there are three problems making the retinal vessel segmentation task even harder. First, the retinal color image tends to be red everywhere, thus having a lower contrast than normal image segmentation. Second, most retinal color images suffer from unbalanced illumination and make it harder to recognize background. Third, the symptom for retinopathy has unexpected color and shape, thus making it more difficult to separate vessel from noises. Under such circumstance, the research of the retinal blood vessel segmentation has brought much attention and been developed. In this work, the input is a retinal color image and the output will be a binary image of the vessel and non-vessel pixels. Preprocessing with CLAHE contrast enhancement, Vessel Extraction with Otsu thresholding is implemented along with performance parameters Accuracy, Sensitivity and Specificity. In the proposed work 20 images are used from the database called Digital Retinal Images for Vessel Extraction. The performance parameters Specificity, Sensitivity, Accuracy are evaluated.

Page No: 138 - 153

References:

1. Zhenwei Li *, Mengli Jia, “Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network”, Micromachines 2021, 12, 1478. https://doi.org/10.3390/ mi12121478.
2. Wang, C.; Zhao, Z.; Ren, Q.; Xu, Y.; Yu, Y. Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation. Entropy 2019, 21, 168.
3. Chen, Y. A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation. arXiv 2017,arXiv:1704.07502.
4. Strisciuglio, N.; Azzopardi, G.; Vento, M.; Petkov, N. Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach. Vis. Appl. 2016, 27, 1137–1149.
5. Guo, C.; Szemenyei, M.; Pei, Y.; Yi, Y.; Zhou, W. SD-U-net: A Structured Dropout U-Net for Retinal Vessel Segmentation. In Proceedings of the IEEE 19th International Conference on Bioinformatics and Bioengineering, Athens, Greece, 28–30 October2019; pp. 439–444.
6. Alom, M.Z.; Yakopcic, C.; Hasan, M.; Taha, T.M.; Asari, V.K. Recurrent residual U-Net for medical image segmentation. J. Med.Imaging 2019, 6, 014006.
7. Wang, X.; Jiang, X. Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework. Signal Process. 2019, 165, 104–114.
8. Yan, Z.; Yang, X.; Cheng, K.T. A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation. IEEE J. Biomed. Health Inform. 2019, 23, 1427–1436.
9. Soomro, T.A.; Afifi, A.J.; Zheng, L.; Soomro, S.; Gao, J.; Hellwich, O.; Paul, M. Deep Learning Models for Retinal Blood Vessels Segmentation: A Review. IEEE Access 2019, 7, 71696–71717.
10. Suryani, E.; Susilo, M. The hybrid method of SOM artificial neural network and median thresholding for segmentation of blood vessels in the retina image fundus. Int. J. Fuzzy Log. Intell. Syst. 2019, 19, 323–331.

How to Cite This Article:
T M SOUJANYA, K PRASAD BABU. IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT & OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING. ijetms;7(1):138-153. DOI: 10.46647/ijetms.2023.v07i01.022