Survey on Automatic Detection of Glaucoma through Deep Learning Using Retinal Fundus Images
Keywords:Glaucoma, Cup to disk ratio, classification, retinal fundus images, Optic disk segmentation, Optic cup segmentation
Glaucoma - a disease which causes damage to our eye's optic nerve and subsequently blinds the vision. This occurs due to increased intraocular pressure (IOP) which causes the damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. Presently, many works have been done towards automatic glaucoma detection using Fundus Images (FI) by extracting structural features. Structural features can be extracted from optic nerve head (ONH) analysis, cup to disc ratio (CDR) and Inferior, Superior, Nasal, Temporal (ISNT) rule in Fundus Image for glaucoma assessment.This survey presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under non-invasive methods of detection. Here, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. The objective of this survey is to obtain a technique which automatically analyze the retinal images of the eye with high efficiency and accuracy
(1) Rongchang Zhao, Xuanlin Chen, Xiyao Liu, Zailiang Chen, Fan Guo and Shuo Li, “Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning”, IEEE Journal of Biomedical and Health Informatics, 12 August 2019
(2) Andres Diaz-Pinto, Adri´an Colomer, Valery Naranjo, Sandra Morales, Yanwu Xu, and Alejandro F Frangi , “Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment”, IEEE Transactions On Medical Imaging, September 2018.
(3) Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, “Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image”, IEEE Transactions On Medical Imaging, Vol. 37, No. 11, November 2018.
(4) T.R. Kausu, Varun P. Gopi, Khan A. Wahid, Wangchuk Doma ,Swamidoss Issac Niwas , “Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images”, ELSEIVER, Biocybernetics And Biomedical Engineering, 2018.
(5) U Raghavendra , Hamido Fujita , Sulatha V Bhandary, Anjan Gudigar , Jen Hong Tan , U Rajendra Acharya , “Deep Convolution Neural Network for Accurate Diagnosis of Glaucoma Using Digital Fundus Images”, ELSEIVER, Information Sciences, 2018.
(6) Anushikha Singha, Malay Kishore Duttaa, M. ParthaSarathia,Vaclav Uherb, Radim Burgetba , “Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image” , ELSEIVER, Computer Methods And Programs in Biomedicine, 2016.
(7) U. Raghavendra & Anjan Gudigar & Sulatha V. Bhandary & Tejaswi N. Rao & Edward J. Ciaccio & U. Rajendra Acharya , “A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images”, SPRINGER, Journal of Medical Systems, 21 July 2019.
(8) Jos´e Denes Lima Ara ´ ujo, Johnatan Carvalho Souza, Otilio Paulo Silva Neto, Jefferson Alves de Sousa, Jo˜ao Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva, Arist ´ ofanes Corrˆea Silva, Geraldo Braz Junior,Marcelo Gattass, “Glaucoma diagnosis in fundus eye images using diversity indexes”, SPRINGER, Multimedia Tools and Applications, 26 July 2018.
(9) Jagadish Nayak & Rajendra Acharya U. & P. Subbanna Bhat & Nakul Shetty & Teik-Cheng Lim , “Automated Diagnosis of Glaucoma Using Digital Fundus Images”, SPRINGER, Journal of Medical Systems, 9 August 2009.
(10) Rüdiger Bock, Jörg Meier, László G. Nyúl, Joachim Hornegger, Georg Michelson , “Glaucoma risk index: Automated glaucoma detection from color fundus images”, ELSEIVER, Medical Image Analysis, 4 January 2010.