A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage

Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems. Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect glaucoma at initial or latter stages, detection strategy and performance. Analysis revealed several research gaps mainly in automated glaucoma detection based on Fundus and Optical Coherence Tomography (OCT) ophthalmic imaging technologies. More accurate diagnosis of glaucoma at early and curable stages is possible by bridging the identified gaps.

[1]  Muhammad Noman Nazir,et al.  Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis , 2020, Journal of Digital Imaging.

[2]  Pritee Khanna,et al.  Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors , 2020, Pattern Recognit. Lett..

[3]  K. Priya,et al.  Glaucoma Detection Using Fundus Images of the Eye , 2020 .

[4]  April Y. Maa,et al.  Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs. , 2018, Ophthalmology.

[5]  Robert N Weinreb,et al.  Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. , 2019, JAMA ophthalmology.

[6]  Paul Sajda,et al.  Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Lola Bautista,et al.  Glaucoma Detection Using Fundus Images of The Eye , 2019, 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA).

[8]  Ji-Hyung Lee,et al.  Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis , 2019, Artif. Intell. Medicine.

[9]  Hideo Yokota,et al.  Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images , 2019, Journal of healthcare engineering.

[10]  U. Rajendra Acharya,et al.  Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques , 2019, Comput. Biol. Medicine.

[11]  Glaucoma Detection Using Fundus Images and OCT Images , 2019, SSRN Electronic Journal.

[12]  Anjan Gudigar,et al.  Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images , 2018, Inf. Sci..

[13]  M. Usman Akram,et al.  Improved automated detection of glaucoma from fundus image using hybrid structural and textural features , 2017, IET Image Process..

[14]  U. Rajendra Acharya,et al.  A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images , 2017, Comput. Biol. Medicine.

[15]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[16]  Debasree Sarkar,et al.  Automated Glaucoma Detection of Medical Image Using Biogeography Based Optimization , 2017 .

[17]  Sunil Kumar Singla,et al.  Detection of Glaucoma Using Retinal Fundus Images , 2016 .

[18]  Qiang Chen,et al.  Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection. , 2015, Optics express.

[19]  T R Ganesh Babu,et al.  Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection. , 2015, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.

[20]  N. Venkateswaran,et al.  Analysis of Segmentation Algorithms in Colour Fundus and OCT Images for Glaucoma Detection , 2015 .

[21]  W. Ruengkitpinyo,et al.  Glaucoma screening using rim width based on ISNT rule , 2015, 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[22]  Dacheng Tao,et al.  Sparse Dissimilarity-Constrained Coding for Glaucoma Screening , 2015, IEEE Transactions on Biomedical Engineering.

[23]  Kevin Noronha,et al.  Decision support system for the glaucoma using Gabor transformation , 2015, Biomed. Signal Process. Control..

[24]  Christopher Bowd,et al.  A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  T. Wong,et al.  Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. , 2014, Ophthalmology.

[26]  Anushikha Singh,et al.  Glaucoma detection by segmenting the super pixels from fundus colour retinal images , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[27]  Jin A. Choi,et al.  Glaucoma diagnostic accuracy of optical coherence tomography parameters in early glaucoma with different types of optic disc damage. , 2014, Ophthalmology.

[28]  David P. Crabb,et al.  Exploring Early Glaucoma and the Visual Field Test: Classification and Clustering Using Bayesian Networks , 2014, IEEE Journal of Biomedical and Health Informatics.

[29]  Christopher Bowd,et al.  Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements , 2014, IEEE Transactions on Biomedical Engineering.

[30]  Hiroshi Ishikawa,et al.  A Method to Estimate Biomechanics and Mechanical Properties of Optic Nerve Head Tissues From Parameters Measurable Using Optical Coherence Tomography , 2014, IEEE Transactions on Medical Imaging.

[31]  N. Thulasi,et al.  Automated diagnosis of glaucoma using Haralick texture features , 2014, International Conference on Information Communication and Embedded Systems (ICICES2014).

[32]  T. C. Manjunath,et al.  Fractal Dimension with Object Rotation: A Case Study with Glaucoma Eye , 2014, 2014 Fifth International Conference on Signal and Image Processing.

[33]  Pina Marziliano,et al.  Angle closure glaucoma detection using fractal dimension index on SS-OCT images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Qiang Chen,et al.  Quantitative assessments of cup-to-disk ratios in spectral domain optical coherence tomography images for glaucoma diagnosis , 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

[35]  Usman Qamar,et al.  Detection of glaucoma using retinal fundus images , 2013, The 6th 2013 Biomedical Engineering International Conference.

[36]  Christopher Bowd,et al.  A Bayesian framework for glaucoma progression detection using Heidelberg Retina Tomograph images , 2013 .

[37]  Jayanthi Sivaswamy,et al.  Motion pattern-based image features for glaucoma detection from retinal images , 2012, ICVGIP '12.

[38]  S. Shenbaga Devi,et al.  Automatic Detection of Glaucoma Using Optical Coherence Tomography Image , 2012 .

[39]  Hiroshi Fujita,et al.  Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  Hye Jin Lee,et al.  Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography. , 2012, Investigative ophthalmology & visual science.

[41]  J. Schuman.  Detection and diagnosis of glaucoma: ocular imaging. , 2012, Investigative ophthalmology & visual science.

[42]  S. R. Krishnadas,et al.  Depth Discontinuity-Based Cup Segmentation From Multiview Color Retinal Images , 2012, IEEE Transactions on Biomedical Engineering.

[43]  U. Rajendra Acharya,et al.  Wavelet-Based Energy Features for Glaucomatous Image Classification , 2012, IEEE Transactions on Information Technology in Biomedicine.

[44]  Tien Yin Wong,et al.  Focal edge association to glaucoma diagnosis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[45]  A. Sukesh Kumar,et al.  Early detection of retinal nerve fiber layer defects using fundus image processing , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[46]  Tun-Wen Pai,et al.  An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma , 2011, 2011 International Conference on Complex, Intelligent, and Software Intensive Systems.

[47]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[48]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features , 2011, IEEE Transactions on Information Technology in Biomedicine.

[49]  David P. Crabb,et al.  Aligning Scan Acquisition Circles in Optical Coherence Tomography Images of The Retinal Nerve Fibre Layer , 2011, IEEE Transactions on Medical Imaging.

[50]  Vassilis Kilintzis,et al.  Symbolic representation of optic nerve head cup presents difference in patients with Primary Open Angle Glaucoma , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[51]  Shijian Lu,et al.  Automated layer segmentation of optical coherence tomography images , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[52]  László G. Nyúl,et al.  Glaucoma risk index:  Automated glaucoma detection from color fundus images , 2010, Medical Image Anal..

[53]  Hiroshi Fujita,et al.  Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images , 2010, Medical Imaging.

[54]  David F. Garway-Heath,et al.  The Pseudotemporal Bootstrap for Predicting Glaucoma From Cross-Sectional Visual Field Data , 2010, IEEE Transactions on Information Technology in Biomedicine.

[55]  G. Ravalico,et al.  Scanning laser polarimetry – a review , 2009, Clinical & experimental ophthalmology.

[56]  Tin Aung,et al.  The prevalence and types of glaucoma in malay people: the Singapore Malay eye study. , 2008, Investigative ophthalmology & visual science.

[57]  A corneal effort mapping system for glaucoma risk based on ocular response analyzer , 2008, 2008 IEEE International Conference on Automation, Quality and Testing, Robotics.

[58]  Shaun Dacosta,et al.  Optic disc topography of normal Indian eyes: An assessment using optical coherence tomography , 2008, Indian journal of ophthalmology.

[59]  Robert N Weinreb,et al.  Risk Assessment in Glaucoma and Ocular Hypertension , 2008, International ophthalmology clinics.

[60]  László G. Nyúl,et al.  Classifying Glaucoma with Image-Based Features from Fundus Photographs , 2007, DAGM-Symposium.

[61]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[62]  Andrew R. McIntyre,et al.  Automated optic nerve analysis for diagnostic support in glaucoma , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[63]  Christopher Kai-shun Leung,et al.  Analysis of retinal nerve fiber layer and optic nerve head in glaucoma with different reference plane offsets, using optical coherence tomography. , 2005, Investigative ophthalmology & visual science.

[64]  Hiroshi Ishikawa,et al.  Comparison of three optical coherence tomography scanning areas for detection of glaucomatous damage. , 2005, American journal of ophthalmology.

[65]  Natalie Hutchings,et al.  A comparison of cup‐to‐disc ratio measurement in normal subjects using optical coherence tomography image analysis of the optic nerve head and stereo fundus biomicroscopy , 2004, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[66]  S. Kingman.  Glaucoma is second leading cause of blindness globally. , 2004, Bulletin of the World Health Organization.

[67]  Andrew R. McIntyre,et al.  Toward glaucoma classification with moment methods , 2004, First Canadian Conference on Computer and Robot Vision, 2004. Proceedings..

[68]  Terrence J. Sejnowski,et al.  Comparison of machine learning and traditional classifiers in glaucoma diagnosis , 2002, IEEE Transactions on Biomedical Engineering.

[69]  Mihaela Ulieru,et al.  Computational intelligence for medical knowledge acquisition with application to glaucoma , 2002, Proceedings First IEEE International Conference on Cognitive Informatics.

[70]  D. Machin,et al.  The prevalence of glaucoma in Chinese residents of Singapore: a cross-sectional population survey of the Tanjong Pagar district. , 2000, Archives of ophthalmology.

[71]  C E Willis,et al.  Glaucoma in optometric practice: a survey of optometrists. , 2000, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[72]  H. Quigley.  Number of people with glaucoma worldwide. , 1996, The British journal of ophthalmology.

[73]  Mary T. Lutz.  The Research Foundation. , 1961, The Journal of the College of General Practitioners.

[74]  S. Sri Abirami,et al.  Glaucoma Images Classification Using Fuzzy Min-Max Neural Network Based On Data-Core , 2022 .

[75]  T. R. Ganesh Babu,et al.  GLAUCOMA DIAGNOSIS OF MORPHOLOGICAL PROCESSING IN OPTICAL COHERENCE TOMOGRAPHY , .