Blood Vessel Detection in the Retina Using Convolution Neural Network

Hoseini, Farnaz; Sepehrzadeh, Hamed; Kheyri, Masume

doi:10.48301/jear.2024.424181.1007

Blood Vessel Detection in the Retina Using Convolution Neural Network

Document Type : Original Article

Authors

Farnaz Hoseini ¹

Hamed Sepehrzadeh ¹

Masume Kheyri ²

¹ Assistant Professor, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

² The Coach, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

10.48301/jear.2024.424181.1007

Abstract

Modern-era developments in authentication systems have changed from traditional methods based on passwords or signatures to new methods based on biometric patterns. Biometric patterns are unique to each person, and identifying individuals has become much more accurate. Biometric cognition uses an intelligent method to identify a person with some unique characteristics of a human being. Unlike traditional methods, these biometric methods are more reliable and safer. Diagnosing blood patterns of retinal images is one of the safest ways to authenticate taking into consideration the monopoly nature of these patterns for each individual and their non-reproducibility and alteration. In the present study, the Convolutional Neural Network (CNN) was used to identify the pattern of blood vessels in the retina. DRIVE dataset was used to evaluate results. The images of the retina of different people were stored in this dataset. After extracting the patterns within the retinal layers for each person as a model indicating the identity of these individuals, the patterns related to the training and testing datasets were compared to determine the identity of individuals. Properly tested samples increase the accuracy of the proposed method, while incorrect detection will cause an error in the proposed method. The results showed that the average accuracy of matching blood vessel patterns for retinal images in the proposed method was 94.83%, which is high and comparable to previous methods.

Keywords

Convolutional Neural Networks

CNN

Blood Vessel

Retina

Authentication System

Subjects

Computer Engineering

[1] Al-Rahawe, E. A. M. (2021). Design of Multimodal Biometric System for Personal Authentication [Ph. D, Swami Ramanand Teerth Marathwada University]. Nanded, India. http ://hdl.handle.net/10603/367594

[2] Papathanasaki, M., Maglaras, L., & Ayres, N. (2022). Modern Authentication Methods: A Comprehensive Survey. Artificial intelligence, Computer Science and Robotics Technology, 2022, 1-24. https://doi.org/10.5772/acrt.08

[3] Portugal, D., Faria, J. N., Belk, M., Martins, P., Constantinides, A., Pietron, A., Pitsillides, A., Avouris, N., & Fidas, C. A. (2023). Continuous user identification in distance learning: a recent technology perspective. Smart Learning Environments, 10(1), 38. https://doi.org/10.1186/s40561-023-00255-9

[4] Rizvi, S., Zwerling, T., Thompson, B., Faiola, S., Campbell, S., Fisanick, S., & Hutnick, C. (2023). A modular framework for auditing IoT devices and networks. Computers & Security, 132(10), 103327. https://doi.org/10.1016/j.cose.2023.103327

[5] Khandouzi, A., Ariafar, A., Mashayekhpour, Z., Pazira, M., & Baleghi, Y. (2022). Retinal Vessel Segmentation, a Review of Classic and Deep Methods. Annals of Biomedical Engineering, 50(10), 1292-1314. https://doi.org/10.1007/s10439-022-03058-0

[6] Marappan, J., Murugesan, K., Elangeeran, M., & Subramanian, U. (2023). Human retinal biometric recognition system based on multiple feature extraction. Journal of Electronic Imaging, 32(1), 013008-013008. https://doi.org/10.1117/1.JEI.32. 1.013008

[7] Sadikoglu, F., & Uzelaltinbulat, S. (2016). Biometric Retina Identification Based on Neural Network. Procedia Computer Science, 102(9), 26-33. https://doi.org/10.1016/j .procs.2016.09.365

[8] Sujatha, V., Anitha, B. S., Rama, G. T., Niharika, N., & Sahithi, A. (2023, February 23-25). Convolutional Neural Network (CNN) based Blood Vessel Segmentation from Ocular Images [Conference session]. 7th International Conference on Computing Methodolo gies and Com munication, Erode, India. https://doi.org/10.1109/ICCMC56507 .2023.10083877

[9] David, S. A., Mahesh, C., Kumar, V. D., Polat, K., Alhudhaif, A., & Nour, M. (2022). Retinal Blood Vessels and Optic Disc Segmentation Using U-Net. Mathematical Problems in Engineering, 2022(7), 1-11. https://doi.org/10.1155/2022/8030954

[10] Naik, S., Kamidi, D., Govathoti, S., Cheruku, R., & Mallikarjuna Reddy, A. (2023). Efficient diabetic retinopathy detection using convolutional neural network and data au gmentation. Soft Computing, 1-12. https://doi.org/10.1007/s00500-023-08537-7

[11] Minija, S. J., Rejula, M. A., & Ross, B. S. (2024). Automated detection of diabetic retinopathy using optimized convolutional neural network. Multimedia Tools and Applications, 83(7), 21065-21080. https://doi.org/10.1007/s11042-023-16204-0

[12] Bharadiya, J. P. (2023). Convolutional neural networks for image classification. International Journal of Innovative Science and Research Technology, 8(5), 673-677. https://d oi.org/10.5281/zenodo.8020781

[13] Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004). Digital Retinal Images for Vessel Extraction [Data set]. Paperswithcode. https:// paperswithcode.com/dataset/drive

[14] Roy, N. D., & Biswas, A. (2020). Fast and robust retinal biometric key generation using deep neural nets. Multimedia Tools and Applications, 79(9), 6823-6843. https:/ /doi.org/10.1007/s11042-019-08507-y

[15] Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019). DUNet: A deformable network for retinal vessel segmentation. Knowledge-Based Systems, 178(5), 149-162. https://doi.org/10.1016/j.knosys.2019.04.025

[16] Tuba, E., Mrkela, L., & Tuba, M. (2017, April 19-20). Retinal blood vessel segmentation by support vector machine classification [Conference session]. 27th International Conference Radioelektronika, Brno, Czech Republic. https://doi.org/10.1109/ RADIOELEK.2017 .7936649

[17] Wang, Y. B., Zhu, C. Z., Yan, Q. F., & Liu, L. Q. (2016, June 24-26). A Novel Vessel Segmentation in Fundus Images Based on SVM [Conference session]. 2016 International Conference on Information System and Artificial Intelligence, Hong Kong, China. https://do i.org/10.1109/ISAI.2016.0089