A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images

H.N. Veena1, A. Muruganandham2, T. Senthil Kumaran1
1Department of Computer Science and Engineering, ACS College of Engineering, VTU, Belagavi, India
2Department of Electronics and Communication Engineering, Raja Rajeswari College of Engineering, VTU, Belagavi, India

Tài liệu tham khảo

Almazroa, 2017, Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction, Clin. Ophthalmol. (Auckland, NZ), 11, 841, 10.2147/OPTH.S117157 A. Almazroa, R. Burman, K. Raahemifar, and V. Lakshminarayanan,” Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey“,Journal of ophthalmology, Vol.2015, no.1, 2015. Arnay, 2017, Ant Colony Optimization-based method for optic cup segmentation in retinal images, Appl. Soft Comput., 52, 409, 10.1016/j.asoc.2016.10.026 Bengani, 2020, Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning, Multimedia Tools and Applications, 1 Bharkad, 2017, Automatic segmentation of optic disk in retinal images, Biomed. Signal Process. Control, 31, 483, 10.1016/j.bspc.2016.09.009 SH Bhat,P Kumar, “Segmentation of optic disc by localized active contour model in retinal fundus image”, In: Smart Innovations in Communication and Computational Sciences, Springer, Singapore, pp. 35-44. 2019. Bian, 2020, Optic disc and optic cup segmentation based on anatomy guided cascade network, Comput. Methods Programs Biomed., 197, 10.1016/j.cmpb.2020.105717 Chen, 2011, Detection of the optic disc on retinal fluorescein angiograms, J Med Biol. Eng, 31, 405, 10.5405/jmbe.773 Chen, 2015, Automatic feature learning for glaucoma detection based on deep learning, 669 Chen, 2015, Glaucoma detection based on deep convolutional neural network, 715 Cheng, 2013, Superpixel classification based optic disc and optic cup segmentation for glaucoma screening, IEEE Trans. Med. Imaging, 32, 1019, 10.1109/TMI.2013.2247770 Chrástek, 2002, Optic disc segmentation in retinal images, Bildverarbeitung für die Medizin, 2002, 263 Civit-Masot, 2020, Dual machine-learning system to aid glaucoma diagnosis using disc and cup feature extraction, IEEE Access, 8, 127519, 10.1109/ACCESS.2020.3008539 A.G.J.M. del Rincón, P. Miller, A.A Blanco, “Automatic Analysis of Digital Retinal Images for Glaucoma Detection”, 2014. M.V.dos Santos Ferreira, A.O.de Carvalho Filho, A.D.de Sousa, A.C. Silva. and M. Gattass. “Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma”,Expert Systems with Applications, Vol.110, pp.250-263,2018. Gillies, 2016, Radiomics: images are more than pictures, They Are Data, 25, 563 Harangi, 2015, Detection of the optic disc in fundus images by combining probability models, Comput. Biol. Med., 65, 10, 10.1016/j.compbiomed.2015.07.002 Hussain, 2020, A unified design of ACO and skewness based brain tumor segmentation and classification from MRI scans, J. Control Eng. Appl. Inform., 22, 43 Jiang, 2019, Jointrcnn: a region-based convolutional neural network for optic disc and cup segmentation, IEEE Trans. Biomed. Eng., 67, 335, 10.1109/TBME.2019.2913211 Jin, 2020, Optic disc segmentation using attention-based U-Net and the improved cross-entropy convolutional neural network, Entropy, 22, 844, 10.3390/e22080844 Joshi, 2010, Optic disk and cup boundary detection using regional information, 948 M.Juneja, S. Singh, N. Agarwal, S. Bali, S. Gupta, N. Thakur and P. Jindal, “Automated detection of Glaucoma using deep learning convolution network (G-net)”,Multimedia Tools and Applications,pp.1-23,2019. Kande, 2008, Segmentation of exudates and optic disk in retinal images, 535 Khan, 2020, Gastrointestinal diseases segmentation and classification based on duo-deep architectures, Pattern Recogn. Lett., 131, 193, 10.1016/j.patrec.2019.12.024 Khan, 2020, Computer-aided gastrointestinal diseases analysis from wireless capsule endoscopy: a framework of best features selection, IEEE Access, 8, 132850, 10.1109/ACCESS.2020.3010448 Khan, Muhammad Attique, Imran Ashraf, Majed Alhaisoni, Robertas Damaševičius, Rafal Scherer, Amjad Rehman, and Syed Ahmad Chan Bukhari. “Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists.”Diagnostics10, no. 8 (2020): 565. Kolossváry, 2015, Advanced atherosclerosis imaging by CT: radiomics, machine learning and deep learning, J. Cardiovasc. Comput. Tomogr., 13, 274, 10.1016/j.jcct.2019.04.007 H. Li and O. Chutatape, “A model-based approach for automated feature extraction in fundus images”, IEEE, Innull, pp. 394, 2003. J. Lowel, A. Hunter, D. Steel, A. Basu, R. Ryder,“IEEE Transactions on medical Imaging, Vol.23, no.2, pp. 256-264,2004. Lu, 2011, Accurate and efficient optic disc detection and segmentation by a circular transformation, IEEE Trans. Med. Imaging, 30, 2126, 10.1109/TMI.2011.2164261 Majid, 2020, Classification of stomach infections: a paradigm of convolutional neural network along with classical features fusion and selection, Microsc. Res. Tech., 83, 562, 10.1002/jemt.23447 Miri, 2015, Mltimodal segmentation of optic disc and cup from SD-OCT and color fundus photographs using a machine-learning graph-based approach, IEEE Trans. Med. Imaging, 34, 1854, 10.1109/TMI.2015.2412881 Mvoulana, 2019, Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images, Comput. Med. Imaging Graph., 77, 10.1016/j.compmedimag.2019.101643 Niemeijer, 2009, Fast detection of the optic disc and fovea in color fundus photographs, Med. Image Anal., 13, 859, 10.1016/j.media.2009.08.003 K.W.obin Jr, E. Chaum, V.P. Govindasamy, T.P.Karnowski and O.Sezer, “Characterization of the optic disc in retinal imagery using a probabilistic approach”, Medical Imaging 2006: Image Processing, vol. 6144, pp. 61443F, 2006. Orlando, 2020, Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs, Med. Image Anal., 59, 10.1016/j.media.2019.101570 Raghavendra, 2018, Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images, Inf. Sci., 441, 41, 10.1016/j.ins.2018.01.051 Rehman, 2020, Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction, Microsc. Res. Tech., 83, 410, 10.1002/jemt.23429 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, pp. 234–241,2015. Sevastopolsky,” Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network”, Pattern Recognition and Image Analysis, Vol. 27,no.3,pp,618–624,2017. Sreng, 2020, Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images, Appl. Sci., 10, 4916, 10.3390/app10144916 Thakur, Niharika, and Mamta Juneja. “Optic disc and optic cup segmentation from retinal images using hybrid approach.”Expert Systems with Applications127 (2019): 308-322. Walter, 2002, A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina, IEEE Trans. Med. Imaging, 21, 1236, 10.1109/TMI.2002.806290 Wong, 2008, Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI, 2266 Yu, 2019, Robust optic disc and cup segmentation with deep learning for glaucoma detection, Comput. Med. Imaging Graph., 74, 61, 10.1016/j.compmedimag.2019.02.005 M.N. Zahoor, and M.M. Fraz, “Fast optic disc segmentation in retina using polar transform”,IEEE Access5, pp.12293-12300. Zhou, Wei, et al. “Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior.”Computational and Mathematical Methods in Medicine2019 (2019). Zhu, 2010, Detection of the optic nerve head in fundus images of the retina using the hough transform for circles, J. Digit. Imaging, 23, 332, 10.1007/s10278-009-9189-5 Zilly, 2015 Zilly, 2018, Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput. Med. Imaging Graph., 55, 28, 10.1016/j.compmedimag.2016.07.012