BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING CONVOLUTIONAL NEURAL NETWORK VV-NET METHOD

Sinta Bella Agustina; Erwin Erwin; Anita  Desiani; Tommy Saputra

doi:10.52436/1.jutif.2024.5.1.1723

Sinta Bella Agustina Master of Computer Science, Departement of Computer Science, Universitas Sriwijaya, Indonesia
Erwin Departement of Computer Engineering, Universitas Sriwijaya, Indonesia
Anita Desiani Departement of Mathematics, Universitas Sriwijaya, Indonesia
Tommy Saputra USS Artificial Intelligence Research Group, Departement of Computer Science, Universitas Sumatera Selatan, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1723

Keywords: Clahe, Convolutional Neural Network, grayscale, median filter, segmentation blood vessels, VV-net

Abstract

The retina is susceptible to various diseases that can be fatal if not treated quickly. Image processing is currently very helpful for doctors to detect retinal diseases faster so that retinal diseases can be treated immediately. The first step in image processing is to improve the quality of retinal images affected by noise, aiming to increase accuracy in the process of segmentation and image extraction. accurate segmentation of retinal blood vessels is the first step in disease detection. The process of segmentation and analysis of retinal blood vessels has an important role in assisting medical professionals in identifying the severity of a disease. Image quality improvement steps in preprocessing use grayscale, median filter (denoising), and clahe. The method used for blood vessel segmentation is CNN VV-Net. Evaluation of the results of applying image quality enhancement and segmentation techniques using the VV-Net method was performed on the DRIVE, STARE, and CHASEDB_1 datasets at both stages, training and testing. The measurement results of blood vessel segmentation using the CNN VV-net method on the DRIVE dataset (accuracy 96.27%, sensitivity 84.38%, precission 75.95%, and jaccard score 66.28%), STARE dataset (accuracy 96.58%, sensitivity 82.78%, precission 76.73%, and jaccard score 65.38%), and CHASEDB_1 dataset (accuracy 97.04%, sensitivity 83.55%, precission 76.72%, and jaccard score 66.40%). From the three datasets used, the CHASEDB_1 dataset obtained better results than the DRIVE and STARE datasets.

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References

J. Lin, X. Huang, H. Zhou, Y. Wang, and Q. Zhang, “Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images,” Med. Image Anal., vol. 89, p. 102929, 2023, doi: 10.1016/j.media.2023.102929.

A. K. Shukla, R. K. Pandey, and R. B. Pachori, “A fractional filter based efficient algorithm for retinal blood vessel segmentation,” Biomed. Signal Process. Control, vol. 59, p. 101883, 2020, doi: 10.1016/j.bspc.2020.101883.

X. Deng and J. Ye, “A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network,” Biomed. Signal Process. Control, vol. 73, no. October 2021, p. 103467, 2022, doi: 10.1016/j.bspc.2021.103467.

F. Tian, “Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology,” Comput. Math. Methods Med., vol. 2021, p. 4761517, 2021, doi: 10.1155/2021/4761517.

K. Naveed, F. Abdullah, H. A. Madni, M. A. U. Khan, T. M. Khan, and S. S. Naqvi, “Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3d filter,” Diagnostics, vol. 11, no. 1, pp. 1–27, 2021, doi: 10.3390/diagnostics11010114.

O. Ramos-Soto et al., “An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering,” Comput. Methods Programs Biomed., vol. 201, p. 105949, 2021, doi: 10.1016/j.cmpb.2021.105949.

O. O. Sule, “A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions,” IEEE Access, vol. 10, no. mild, pp. 38202–38236, 2022, doi: 10.1109/ACCESS.2022.3163247.

C. Chen, J. H. Chuah, R. Ali, and Y. Wang, “Retinal vessel segmentation using deep learning: A review,” IEEE Access, vol. 9, pp. 111985–112004, 2021, doi: 10.1109/ACCESS.2021.3102176.

B. Saha Tchinda, D. Tchiotsop, M. Noubom, V. Louis-Dorr, and D. Wolf, “Retinal blood vessels segmentation using classical edge detection filters and the neural network,” Informatics Med. Unlocked, vol. 23, p. 100521, 2021, doi: 10.1016/j.imu.2021.100521.

B. Toptaş and D. Hanbay, “Retinal blood vessel segmentation using pixel-based feature vector,” Biomed. Signal Process. Control, vol. 70, p. 103053, 2021, doi: https://doi.org/10.1016/j.bspc.2021.103053.

Q. Qin and Y. Chen, “A review of retinal vessel segmentation for fundus image analysis,” Eng. Appl. Artif. Intell., vol. 128, no. November 2023, p. 107454, 2024, doi: 10.1016/j.engappai.2023.107454.

L. Hakim, M. S. Kavitha, N. Yudistira, and T. Kurita, “Regularizer based on Euler characteristic for retinal blood vessel segmentation,” Pattern Recognit. Lett., vol. 149, pp. 83–90, 2021, doi: https://doi.org/10.1016/j.patrec.2021.05.023.

Y. Ma et al., “ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 928–939, 2021, doi: 10.1109/TMI.2020.3042802.

A. Reyes-Figueroa and M. Rivera, “W–net: A Convolutional Neural Network for Retinal Vessel Segmentation,” in Pattern Recognition, 2021, pp. 355–368. doi: 10.1007/978-3-030-77004-4_34.

A. Desiani, Erwin, B. Suprihatin, and S. B. Agustina, “a Robust Techniques of Enhancement and Segmentation Blood Vessels in Retinal Image Using Deep Learning,” Biomed. Eng. - Appl. Basis Commun., vol. 34, no. 4, pp. 1–9, 2022, doi: 10.4015/S1016237222500193.

A. Imran, J. Li, Y. Pei, J. J. Yang, and Q. Wang, “Comparative Analysis of Vessel Segmentation Techniques in Retinal Images,” IEEE Access, vol. 7, pp. 114862–114887, 2019, doi: 10.1109/ACCESS.2019.2935912.

A. Abdulrahman and S. Varol, “A Review of Image Segmentation Using MATLAB Environment,” 8th Int. Symp. Digit. Forensics Secur. ISDFS 2020, pp. 8–12, 2020, doi: 10.1109/ISDFS49300.2020.9116191.

Y. Zhu and C. Huang, “An Improved Median Filtering Algorithm for Image Noise Reduction,” Phys. Procedia, vol. 25, pp. 609–616, 2012, doi: 10.1016/j.phpro.2012.03.133.

J. Ouyang, S. Liu, H. Peng, H. Garg, and D. N. H. Thanh, “LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation,” Complex Intell. Syst., 2023, doi: 10.1007/s40747-023-01095-3.

J. Li, G. Gao, L. Yang, and Y. Liu, “GDF-Net: A multi-task symmetrical network for retinal vessel segmentation,” Biomed. Signal Process. Control, vol. 81, no. August 2022, p. 104426, 2023, doi: 10.1016/j.bspc.2022.104426.

H. Du et al., “Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method,” Comput. Biol. Med., vol. 153, p. 106416, 2023, doi: 10.1016/j.compbiomed.2022.106416.

Y. Zhang, M. He, Z. Chen, K. Hu, X. Li, and X. Gao, “Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation,” Expert Syst. Appl., vol. 195, p. 116526, 2022, doi: https://doi.org/10.1016/j.eswa.2022.116526.

S. Mahapatra, S. Agrawal, P. K. Mishro, and R. B. Pachori, “A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM,” Comput. Biol. Med., vol. 147, no. June, p. 105770, 2022, doi: 10.1016/j.compbiomed.2022.105770.

F. Dong, D. Wu, C. Guo, S. Zhang, B. Yang, and X. Gong, “CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation,” Comput. Biol. Med., vol. 147, no. February, p. 105651, 2022, doi: 10.1016/j.compbiomed.2022.105651.

A. Desiani, Erwin, B. Suprihatin, F. Efriliyanti, M. Arhami, and E. Setyaningsih, “VG-DropDNet A Robust Architecture for Blood Vessels Segmentation on Retinal Image,” IEEE Access, vol. 10, no. September, pp. 1–1, 2022, doi: 10.1109/access.2022.3202890.

D. E. Alvarado-Carrillo and O. S. Dalmau-Cedeño, “Width Attention based Convolutional Neural Network for Retinal Vessel Segmentation,” Expert Syst. Appl., vol. 209, p. 118313, 2022, doi: 10.1016/j.eswa.2022.118313.

V. Sathananthavathi and G. Indumathi, “Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation,” Cogn. Syst. Res., vol. 67, pp. 84–95, 2021, doi: 10.1016/j.cogsys.2021.01.003.

M. E. Gegundez-Arias, D. Marin-Santos, I. Perez-Borrero, and M. J. Vasallo-Vazquez, “A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model,” Comput. Methods Programs Biomed., vol. 205, p. 106081, 2021, doi: 10.1016/j.cmpb.2021.106081.

E. Abdelmaksoud, S. El-Sappagh, S. Barakat, T. Abuhmed, and M. Elmogy, “Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions,” IEEE Access, vol. 9, no. Vl, pp. 15939–15960, 2021, doi: 10.1109/ACCESS.2021.3052870.