International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.15, No.1, Feb. 2023
Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture
Full Text (PDF, 979KB), PP.79-92
Diabetic retinopathy is one of the most serious eye diseases and can lead to permanent blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will develop diabetic retinopathy, but the risk of developing diabetes is undeniable. This requires the early diagnosis of Diabetic retinopathy. Segmentation is one of the approaches which is useful for detecting the blood vessels in the retinal image. This paper proposed the three models based on a deep learning approach for recognizing blood vessels from retinal images using region-based segmentation techniques. The proposed model consists of four steps preprocessing, Augmentation, Model training, and Performance measure. The augmented retinal images are fed to the three models for training and finally, get the segmented image. The proposed three models are applied on publically available data set of DRIVE, STARE, and HRF. It is observed that more thin blood vessels are segmented on the retinal image in the HRF dataset using model-3. The performance of proposed three models is compare with other state-of-art-methods of blood vessels segmentation of DRIVE, STARE, and HRF datasets.
Cite This Paper
Swapnil V. Deshmukh, Apash Roy, Pratik Agrawal, "Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.1, pp. 79-92, 2023. DOI:10.5815/ijigsp.2023.01.07
S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing, vol. 149, pp. 708-717, 2015.
M. v. Grinsven, B. v. Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sanchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1273-1284, 2016.
P. Liu, H. Zhang, and K. B. Eom, “Active Deep Learning for Classification of Hyperspectral Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 99, 2016.
Y. Chen, X. Zhao, and X. Jia, “Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, 2015.
S V Deshmukh and A Roy "An Empirical Exploration of Artificial Intelligence in Medical Domain for Prediction and Analysis of Diabetic Retinopathy: Review", 2021, International Conference on Robotics and Artificial Intelligence (RoAI) 2020, 1831 (2021) 012012, doi:10.1088/1742-6596/1831/1/012012
Wang, Y., Ji, G., Lin, P. & Trucco, E. 2013. Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognition, 46, 2117-2133.
Zolfagharnasab, H., & Naghsh-Nilchi, A. R. (2014). Cauchy-based matched filter for retinal vessels detection. Journal of medical signals and sensors, 4(1), 1–9.
M. Frucci, D. Riccio, G. S. Di Baja and L. Serino, "Using Contrast and Directional Information for Retinal Vessels Segmentation," 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014, pp. 592-597, doi: 10.1109/SITIS.2014.18.
S. K. Vengalil, N. Sinha, S. S. S. Kruthiventi and R. V. Babu, "Customizing CNNs for blood vessel segmentation from fundus images," 2016 International Conference on Signal Processing and Communications (SPCOM), 2016, pp. 1-4, doi: 10.1109/SPCOM.2016.7746702.
P. Liskowski and K. Krawiec, "Segmenting Retinal Blood Vessels with Deep Neural Networks," in IEEE Transactions on Medical Imaging, vol. 35, no. 11, pp. 2369-2380, Nov. 2016, doi: 10.1109/TMI.2016.2546227.
Jiang, Z., Yepez, J., An, S. & Ko, S. 2017. Fast, accurate and robust retinal vessel segmentation system. Biocybernetics and Biomedical Engineering, 37, 412-421.
Oliveira, A., Pereira, S. & Silva, C. A. 2018. Retinal vessel segmentation based on fully convolutional neural networks. Expert Systems with Applications, 112, 229-242.
Sathananthavathi, V. & Indumathi, G. 2018. BAT algorithm inspired retinal blood vessel segmentation. IET Image Processing, 12, 2075-2083.
C. -H. Hua, T. Huynh-The and S. Lee, "Retinal Vessel Segmentation using Round-wise Features Aggregation on Bracket-shaped Convolutional Neural Networks," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 36-39, DOI: 10.1109/EMBC.2019.8856552.
Tian, C., Fang, T., Fan, Y. & Wu, W. 2020. Multi-path convolutional neural network in fundus segmentation of blood vessels. Biocybernetics and Biomedical Engineering, 40, 583-595.
Azzopardi, George; Strisciuglio, Nicola; Vento, Mario; Petkov, Nicolai (2015). Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical Image Analysis, 19(1), 46–57. DOI: 10.1016/j.media.2014.08.002
Maninis, KK., Pont-Tuset, J., Arbeláez, P., Van Gool, L. (2016). Deep Retinal Image Understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science (), vol 9901. Springer, Cham. https://doi.org/10.1007/978-3-319-46723-8_17.
Vlachos, M. & Dermatas, E. 2010. Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics, Volume 34, Issue 3, April 2010, Pages 213-227. https://doi.org/10.1016/j.compmedimag.2009.09.006
A. M. Mendonca and A. Campilho, "Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction," in IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1200-1213, Sept. 2006, DOI: 10.1109/TMI.2006.879955.
Mo J, Zhang L. Multi-level deep supervised networks for retinal vessel segmentation. Int J Comput Assist Radiol Surg. 2017 Dec;12(12):2181-2193. DOI: 10.1007/s11548-017-1619-0. Epub 2017 Jun 2. PMID: 28577175.
Li, Liangzhi & Verma, Manisha & Nakashima, Yuta & Nagahara, Hajime & Kawasaki, Ryo. (2020). IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. 3645-3654. DOI: 10.1109/WACV45572.2020.9093621.
K. B. Park, S. H. Choi and J. Y. Lee, "M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses Through Stacked Deep Fully Convolutional Networks," in IEEE Access, vol. 8, pp. 146308-146322, 2020, DOI: 10.1109/ACCESS.2020.3015108.
K.S. Sreejini, V.K. Govindan, Retrieval of pathological retina images using Bag of Visual Words and pLSA model, Engineering Science and Technology, an International Journal, Volume 22, Issue 3, 2019, Pages 777-785, https://doi.org/10.1016/j.jestch.2019.02.002.
İbrahim Atli, Osman Serdar Gedik, Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation, Engineering Science and Technology, an International Journal, Volume 24, Issue 2, 2021, Pages 271-283, https://doi.org/10.1016/j.jestch.2020.07.008.
Álvaro S. Hervella, José Rouco, Jorge Novo, Marcos Ortega, Multimodal image encoding pre-training for diabetic retinopathy grading, Computers in Biology and Medicine, Volume 143, 2022, 105302., https://doi.org/10.1016/j.compbiomed.2022.105302.
Azat Garifullin, Lasse Lensu, Hannu Uusitalo, Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges, Computers in Biology and Medicine, Volume 136, 2021, 104725, https://doi.org/10.1016/j.compbiomed.2021.104725.
Jie Xue, Shuo Yan, Jianhua Qu, Feng Qi, Chenggong Qiu, Meirong Chen, Tingting Liu, Dengwang Li, Xiyu Liu, Deep membrane systems for multitasking segmentation in diabetic retinopathy, Knowledge-Based Systems (2019), DOI: https://doi.org/10.1016/j.knosys.2019.104887.
Kemal Adem, Exudate Detection for Diabetic Retinopathy with Circular Hough Transformation and Convolutional Neural Networks, Expert Systems with Applications (2018), doi: 10.1016/j.eswa.2018.07.053
T. Jemima Jebaseeli, C. Anand Deva Durai, J. Dinesh Peter, Segmentation of retinal blood vessels from ophthalmologic Diabetic Retinopathy images, Computers & Electrical Engineering, Volume 73, 2019, Pages 245-258, https://doi.org/10.1016/j.compeleceng.2018.11.024.
Shaohua Wan, Yan Liang, Yin Zhang, Deep convolutional neural networks for diabetic retinopathy detection by image classification, Computers & Electrical Engineering, Volume 72, 2018, Pages 274-282, https://doi.org/10.1016/j.compeleceng.2018.07.042.
Zaixing Jiang, Junhui Wang, Craig S. Fulthorpe, Li’an Liu, Yuanfu Zhang, Huimin Liu, A quantitative model of paleowind reconstruction using subsurface lacustrine longshore bar deposits – An attempt, Sedimentary Geology, Volume 371, 2018, Pages 1-15, https://doi.org/10.1016/j.sedgeo.2018.04.004.
G. Huang, Z. Liu, L. Van Der Maaten and K. Weinberger, "Densely Connected Convolutional Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017 pp. 2261-2269. DOI: 10.1109/CVPR.2017.243.
S. Feng, Z. Zhuo, D. Pan, Q. Tian, CcNet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features, Neurocomputing (2019).
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (2015), pp. 234-241.
I. Atli, O.S. Gedik, VESUNETDeep: A fully convolutional deep learning architecture for automated vessel segmentation, in: 2019 27th Signal Processing and Communications Applications Conference, 2019, pp. 1-4.
Y. Zhang, A.C. Chung, Deep supervision with additional labels for retinal vessel segmentation task, in: International conference on medical image computing and computer-assisted intervention (2018), pp. 83-91.
Gourav, Tejpal Sharma and Harsmeet Singh, "Computational Approach to Image Segmentation Analysis", I.J. Modern Education and Computer Science, 2017, 7, 30-37 Published Online July 2017 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2017.07.04
Gourav, Tejpal Sharma, "Various Types of Image Noise and De-noising Algorithm", I.J. Modern Education and Computer Science, 2017, 5, 50-58 Published Online May 2017 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2017.05.07
Shiv Gehlot, John Deva Kumar, "The Image Segmentation Techniques", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.9, No.2, pp.9-18, 2017.DOI: 10.5815/ijigsp.2017.02.02