PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Retinal image analysis is crucially important to detect the different kinds of life-threatening cardiovascular and ophthalmic diseases as human retinal microvasculature exhibits remarkable abnormalities responding to these disorders. The high dimensionality and random accumulation of retinal images enlarge the data size, that creating complexity in managing and understating the retinal image data. Deep Learning (DL) has been introduced to deal with this big data challenge by developing intelligent tools. Convolutional Neural Network (CNN), a DL approach, has been designed to extract hierarchical image features with more abstraction. To assist the ophthalmologist in eye screening and ophthalmic disease diagnosis, CNN is being explored to create automatic systems for microvascular pattern analysis, feature extraction, and quantification of retinal images. Extraction of the true vessel of retinal microvasculature is significant for further analysis, such as vessel diameter and bifurcation angle quantification. This study proposes a retinal image feature, true vessel segments extraction approach exploiting the Faster RCNN. The fundamental Image Processing principles have been employed for pre-processing the retinal image data. A combined database assembling image data from different publicly available databases have been used to train, test, and evaluate this proposed method. This proposed method has obtained 92.81% sensitivity and 63.34 positive predictive value in extracting true vessel segments from the top first tier of colour retinal images. It is expected to integrate this method into ophthalmic diagnostic tools with further evaluation and validation by analysing the performance.

• Abadi, M., McMahan, H. B., Chu, A., Mironov, I., Zhang, L., Goodfellow, I., & Talwar, K. (2016). Deep learning with differential privacy. Proceedings of the ACM Conference on Computer and Communications Security, 24-28-Octo(Ccs), 308-318. https://doi.org/10.1145/2976749.2978318

• Abbasi-sureshjani, M. F. S., Romeny, H., & Sarti, A. (2016). Analysis of vessel connectivities in retinal images by cortically inspired spectral clustering. Journal of Mathematical Imaging and Vision, 56(1), 158-172. https://doi.org/10.1007/s10851-016-0640-1

• Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016). Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology and Visual Science, 57(13), 5200-5206. https://doi.org/10.1167/iovs.16-19964

• Al-Diri, B., Hunter, A., Steel, D., Habib, M., Hudaib, T., & Berry, S. (2008). A reference data set for retinal vessel profiles. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2262-2265). IEEE Publishing. https://doi.org/10.1109/IEMBS.2008.4649647

• Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. ArXiv Publishing.

• Badawi, S. A., & Fraz, M. M. (2019). Multiloss function based deep convolutional neural network for segmentation of retinal vasculature into arterioles and venules. BioMed Research International, 2019, Article 4747230. https://doi.org/10.1155/2019/4747230

• Baker, M. L., Hand, P. J., Wang, J. J., & Wong, T. Y. (2008). Retinal signs and stroke: Revisiting the link between the eye and brain. Stroke, 39(4), 1371-1379. https://doi.org/10.1161/STROKEAHA.107.496091

• Buduma, N., & Locascio, N. (2017). Fundamentals of deep learning: Designing next-generation machine intelligence algorithm. O’Reilly Media Inc.

• De Silva, D. A., Manzano, J. J. F., Liu, E. Y., Woon, F. P., Wong, W. X., Chang, H. M., Chen, C., Lindley, R. I., Wang, J. J., Mitchell, P., Wong, T. Y., & Wong, M. C. (2011). Retinal microvascular changes and subsequent vascular events after ischemic stroke. Neurology, 77(9), 896-903. https://doi.org/10.1212/WNL.0b013e31822c623b

• Fenner, B. J., Wong, R. L. M., Lam, W. C., Tan, G. S. W., & Cheung, G. C. M. (2018). Advances in retinal imaging and applications in diabetic retinopathy screening: A review. Ophthalmology and Therapy, 7(2), 333-346. https://doi.org/10.1007/s40123-018-0153-7

• García, M., López, M. I., Álvarez, D., & Hornero, R. (2010). Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. Medical Engineering and Physics, 32(10), 1085-1093. https://doi.org/10.1016/j.medengphy.2010.07.014

• García, M., Sánchez, C. I., Poza, J., López, M. I., & Hornero, R. (2009a). Detection of hard exudates in retinal images using a radial basis function classifier. Annals of Biomedical Engineering, 37(7), 1448-1463. https://doi.org/10.1007/s10439-009-9707-0

• García, M., Sánchez, C. I., López, M. I., Abásolo, D., & Hornero, R. (2009b). Neural network based detection of hard exudates in retinal images. Computer Methods and Programs in Biomedicine, 93(1), 9-19. https://doi.org/10.1016/j.cmpb.2008.07.006

• Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969. https://doi.org/10.1016/j.ophtha.2017.02.008

• Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016). Marginal space deep learning: Efficient architecture for volumetric image parsing. IEEE Transactions on Medical Imaging, 35(5), 1217-1228. https://doi.org/10.1109/TMI.2016.2538802

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

• Grassmann, F., Mengelkamp, J., Brandl, C., Harsch, S., Zimmermann, M. E., Linkohr, B., Peters, A., Heid, I. M., Palm, C., & Weber, B. H. F. (2018). A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology, 125(9), 1410-1420. https://doi.org/10.1016/j.ophtha.2018.02.037

• Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA - Journal of the American Medical Association, 316(22), 2402-2410. https://doi.org/10.1001/jama.2016.17216

• Guo, S., Wang, K., Kang, H., Zhang, Y., Gao, Y., & Li, T. (2019). BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation. International Journal of Medical Informatics, 126, 105-113. https://doi.org/10.1016/j.ijmedinf.2019.03.015

• Henderson, A. D., Bruce, B. B., Newman, N. J., & Biousse, V. (2011). Hypertension-related eye abnormalities and the risk of stroke. Reviews in Neurological Diseases, 8(404), 1-9. https://doi.org/10.3909/rind0274

• Hoque, M. E., Kipli, K., Zulcaffle, T. M. A., Mat, D. A. A., Joseph, A., Zamhari, N., Sapawi, R., & Arafat, M. Y. (2019). Segmentation of retinal microvasculature based on iterative self-organizing data analysis technique (ISODATA). In 2019 International UNIMAS STEM 12th Engineering Conference (EnCon) (pp. 59-64). IEEE Publishing. https://doi.org/10.1109/EnCon.2019.8861259

• Hoque, M. E., Kipli, K., Zulcaffle, T. M. A., Sapawi, R., Joseph, A., Abidin, W. A. W. Z., & Sahari, S. K. (2018). Feature extraction method of retinal vessel diameter. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 279-283). IEEE Publishing. https://doi.org/10.1109/IECBES.2018.8626660

• James, M. (2000). Cost effectiveness analysis of screening for sight-threatening diabetic eye disease. BMJ, 320(7250), 1627-1631. https://doi.org/10.1136/bmj.320.7250.1627

• Kipli, K., Hoque, M. E., Lim, L. T., Mahmood, M. H., Sahari, S. K., Sapawi, R., Rajaee, N., & Joseph, A. (2018). A review on the extraction of quantitative retinal microvascular image feature. Computational and Mathematical Methods in Medicine, 2018, Article 4019538. https://doi.org/10.1155/2018/4019538

• Kipli, K., Hoque, M. E., Lim, L. T., Zulcaffle, T. M. A., Sahari, S. K., & Mahmood, M. H. (2020). Retinal image blood vessel extraction and quantification with Euclidean distance transform approach. IET Image Processing, 14(15), 3718-3724.. https://doi.org/10.1049/iet-ipr.2020.0336

• Krittanawong, C., Zhang, H. J., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571

• Lahiri, A., Roy, A. G., Sheet, D., & Biswas, P. K. (2016). Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography. In 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 1340-1343). IEEE Publishing. https://doi.org/10.1109/EMBC.2016.7590955

• Maji, D., Santara, A., Ghosh, S., Sheet, D., & Mitra, P. (2015). Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. In 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3029-3032). IEEE Publishing. https://doi.org/10.1109/EMBC.2015.7319030

• Melinsca, M., Prentasic, P., & Loncaric, S. (2015). Retinal vessel segmentation using deep neural networks. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015) (pp. 577-582). Science and Technology Publications. https://doi.org/10.5220/0005313005770582

• Mo, J., & Zhang, L. (2017). Multi-level deep supervised networks for retinal vessel segmentation. International Journal of Computer Assisted Radiology and Surgery, 12(12), 2181-2193. https://doi.org/10.1007/s11548-017-1619-0

• Niemeijer, M., Van Ginneken, B., Russell, S. R., Suttorp-Schulten, M. S. A., & Abràmoff, M. D. (2007). Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative Ophthalmology and Visual Science, 48(5), 2260-2267. https://doi.org/10.1167/iovs.06-0996

• Oliveira, A., Pereira, S., & Silva, C. A. (2018). Retinal vessel segmentation based on fully convolutional neural networks. Expert Systems with Applications, 112, 229-242. https://doi.org/10.1016/j.eswa.2018.06.034

• Ong, Y. T., Wong, T. Y., Klein, R., Klein, B. E., Mitchell, P., Sharrett, A. R., Couper, D. J., & Ikram, M. K. (2013). Hypertensive retinopathy and risk of stroke. Hypertension, 62(4), 706-711. https://doi.org/10.1161/HYPERTENSIONAHA.113.01414

• Osareh, A., Mirmehdi, M., Thomas, B., & Markham, R. (2003). Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology, 87(10), 1220-1223. http://dx.doi.org/10.1136/bjo.87.10.1220

• Osareh, A., Shadgar, B., & Markham, R. (2009). A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images. IEEE Transactions on Information Technology in Biomedicine, 13(4), 535-545. https://doi.org/10.1109/TITB.2008.2007493

• Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90(July), 200-205. https://doi.org/10.1016/j.procs.2016.07.014

• Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031

• Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018). Artificial intelligence in retina. Progress in Retinal and Eye Research, 67(May), 1-29. https://doi.org/10.1016/j.preteyeres.2018.07.004

• Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS ONE, 12(6), 1-11. https://doi.org/10.1371/journal.pone.0179790

• Tan, J. H., Fujita, H., Sivaprasad, S., Bhandary, S. V., Rao, A. K., Chua, K. C., & Acharya, U. R. (2017). Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Information Sciences, 420(August), 66-76. https://doi.org/10.1016/j.ins.2017.08.050

• Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., Yeo, I. Y. S., Lee, S. Y., Wong, E. Y. M., Sabanayagam, C., Baskaran, M., Ibrahim, F., Tan, N. C., Finkelstein, E. A., Lamoureux, E. L., Wong, I. Y., Bressler, N. M., … & Wong, T. Y. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA - Journal of the American Medical Association, 318(22), 2211-2223. https://doi.org/10.1001/jama.2017.18152

• Van Grinsven, M. J. J. P., Van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE Transactions on Medical Imaging, 35(5), 1273-1284. https://doi.org/10.1109/TMI.2016.2526689

• Wang, C., Zhao, Z., Ren, Q., Xu, Y., & Yu, Y. (2019). Dense U-net based on patch-based learning for retinal vessel segmentation. Entropy, 21(2), 1-15. https://doi.org/10.3390/e21020168

• Wang, J. J., Baker, M. L., Hand, P. J., Hankey, G. J., Lindley, R. I., Rochtchina, E., Wong, T. Y., Liew, G., & Mitchell, P. (2011). Transient ischemic attack and acute ischemic stroke: Associations with retinal microvascular signs. Stroke, 42(2), 404-408. https://doi.org/10.1161/STROKEAHA.110.598599

• Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., & Yang, G. (2015). Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 149(PB), 708-717. https://doi.org/10.1016/j.neucom.2014.07.059

• Witt, N., Wong, T. Y., Hughes, A. D., Chaturvedi, N., Klein, B. E., Evans, R., McNamara, M., McG Thom, S. A., & Klein, R. (2006). Abnormalities of retinal microvascular structure and risk of mortality from ischemic heart disease and stroke. Hypertension, 47(5), 975-981. https://doi.org/10.1161/01.HYP.0000216717.72048.6c

• Yan, Z., Yang, X., & Cheng, K. T. (2019). A three-stage deep learning model for accurate retinal vessel segmentation. IEEE Journal of Biomedical and Health Informatics, 23(4), 1427-1436. https://doi.org/10.1109/JBHI.2018.2872813

• Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z., & Liang, Y. (2017). Retinal vessel segmentation in colour fundus images using Extreme Learning Machine. Computerized Medical Imaging and Graphics, 55(2017), 68-77. https://doi.org/10.1016/j.compmedimag.2016.05.004