Computer-aided diagnosis of diabetic retinopathy: A review

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.

[1]  Uğur Şevik,et al.  Automatic segmentation of age-related macular degeneration in retinal fundus images , 2008, Comput. Biol. Medicine.

[2]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[3]  Jeffrey E. Boyd,et al.  Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Peter K Kaiser,et al.  Optical coherence tomographic patterns of diabetic macular edema. , 2006, American journal of ophthalmology.

[5]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[6]  Bálint Antal,et al.  An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading , 2012, IEEE Transactions on Biomedical Engineering.

[7]  Larry N. Thibos,et al.  The mechanisms of vision loss associated with a cotton wool spot , 2009, Vision Research.

[8]  C. M. Lim,et al.  Computer-based detection of diabetes retinopathy stages using digital fundus images , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[9]  Miguel J Maldonado,et al.  Retinal thickness study with optical coherence tomography in patients with diabetes. , 2002, Investigative ophthalmology & visual science.

[10]  Peter F Sharp,et al.  A fully automated comparative microaneurysm digital detection system , 1997, Eye.

[11]  Frans Coenen,et al.  Image Classification Using Histograms and Time Series Analysis: A Study of Age-Related Macular Degeneration Screening in Retinal Image Data , 2010, ICDM.

[12]  Bram van Ginneken,et al.  Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus Photographs , 2009, IEEE Transactions on Medical Imaging.

[13]  E Reichel,et al.  Topography of diabetic macular edema with optical coherence tomography. , 1998, Ophthalmology.

[14]  Gwénolé Quellec,et al.  Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal Images , 2011, IEEE Transactions on Medical Imaging.

[15]  J. Olson,et al.  Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool , 2000, Diabetic medicine : a journal of the British Diabetic Association.

[16]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[17]  Alireza Osareh,et al.  AUTOMATIC BLOOD VESSEL SEGMENTATION IN COLOR IMAGES OF RETINA , 2009 .

[18]  Andrew Hunter,et al.  An Active Contour Model for Segmenting and Measuring Retinal Vessels , 2009, IEEE Transactions on Medical Imaging.

[19]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[20]  Roberto Hornero,et al.  A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. , 2008, Medical engineering & physics.

[21]  T. Tamura,et al.  An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes , 2013, Computer methods in biomechanics and biomedical engineering.

[22]  Chua Kuang Chua,et al.  Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[23]  Joel S Schuman,et al.  Automated detection of clinically significant macular edema by grid scanning optical coherence tomography. , 2006, Ophthalmology.

[24]  U. Acharya,et al.  Automatic identification of diabetic maculopathy stages using fundus images , 2009, Journal of medical engineering & technology.

[25]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[26]  J. Olson,et al.  The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme , 2007, British Journal of Ophthalmology.

[27]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[28]  C. Sinthanayothin,et al.  Automated detection of diabetic retinopathy on digital fundus images , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[29]  Bram van Ginneken,et al.  Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.

[30]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[31]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[32]  Langis Gagnon,et al.  Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching , 2001, IEEE Transactions on Medical Imaging.

[33]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[34]  J. Olson,et al.  Automated detection of exudates for diabetic retinopathy screening , 2007, Physics in medicine and biology.

[35]  U. Rajendra Acharya,et al.  Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images , 2008, Journal of Medical Systems.

[36]  Rushmia Karim,et al.  Use of antivascular endothelial growth factor for diabetic macular edema , 2010, Clinical ophthalmology.

[37]  Xiaohui Zhang,et al.  A SVM approach for detection of hemorrhages in background diabetic retinopathy , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[38]  A. S. Neubauer,et al.  Screening der diabetischen Retinopathie und Papillentopographie mit dem „Retinal Thickness Analyzer“ (RTA) , 2004, Der Ophthalmologe.

[39]  C. Thivolet,et al.  Screening of diabetic retinopathy: effect of field number and mydriasis on sensitivity and specificity of digital fundus photography. , 2008, Diabetes & metabolism.

[40]  S. Nussey,et al.  Assessment of Automated Disease Detection in Diabetic Retinopathy Screening Using Two-Field Photography , 2011, PloS one.

[41]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[42]  U. Rajendra Acharya,et al.  Identification of different stages of diabetic retinopathy using retinal optical images , 2008, Inf. Sci..

[43]  Peter F. Sharp,et al.  Automated microaneurysm detection using local contrast normalization and local vessel detection , 2006, IEEE Transactions on Medical Imaging.

[44]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[45]  J. Olson,et al.  Automatic detection of retinal anatomy to assist diabetic retinopathy screening , 2007, Physics in medicine and biology.

[46]  Isabelle Bloch,et al.  A Morphological Approach for Vessel Segmentation in Eye Fundus Images, with Quantitative Evaluation , 2011 .

[47]  Philip J. Morrow,et al.  Algorithms for digital image processing in diabetic retinopathy , 2009, Comput. Medical Imaging Graph..

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

[49]  T Morris,et al.  Progress towards automated detection and characterization of the optic disc in glaucoma and diabetic retinopathy , 2006, Medical informatics and the Internet in medicine.

[50]  U. Rajendra Acharya,et al.  Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features , 2012, Knowl. Based Syst..

[51]  Majid Mirmehdi,et al.  Classification and Localisation of Diabetic-Related Eye Disease , 2002, ECCV.

[52]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[53]  Yin Aye Moe,et al.  Automatic Exudate Detection with a Naive Bayes Classifier , 2008 .

[54]  Chanjira Sinthanayothin,et al.  Automated screening system for diabetic retinopathy , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[55]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[56]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[57]  Michael Larsen,et al.  Diabetic macular edema assessed with optical coherence tomography and stereo fundus photography. , 2002, Investigative ophthalmology & visual science.

[58]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on Medical Imaging.

[59]  Michael Larsen,et al.  Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis. , 2004, Acta ophthalmologica Scandinavica.

[60]  Lila Iznita Izhar,et al.  Determination of foveal avascular zone in diabetic retinopathy digital fundus images , 2010, Comput. Biol. Medicine.

[61]  T. Williamson,et al.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.

[62]  Andrew Hunter,et al.  Optic nerve head segmentation , 2004, IEEE Transactions on Medical Imaging.

[63]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[64]  LeongWen-Fung,et al.  A Sorting System for Hierarchical Grading of Diabetic Fundus Images , 2008 .

[65]  D McLeod,et al.  Why cotton wool spots should not be regarded as retinal nerve fibre layer infarcts , 2005, British Journal of Ophthalmology.

[66]  Atsushi Mizutani,et al.  Automated microaneurysm detection method based on double ring filter in retinal fundus images , 2009, Medical Imaging.

[67]  Christos Haritoglou,et al.  Topography of diabetic macular oedema compared with fluorescein angiography. , 2006, Acta ophthalmologica Scandinavica.

[68]  M. Sonka,et al.  Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.

[69]  Peter F. Sharp,et al.  Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes , 2008, Diabetes Care.

[70]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..

[71]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[72]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[73]  A. Alghadyan,et al.  Diabetic retinopathy - An update. , 2011, Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society.

[74]  P Gain,et al.  Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. , 2010, Diabetes & metabolism.

[75]  David Moher,et al.  Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Standards for Reporting of Diagnostic Accuracy. , 2003, Clinical chemistry.

[76]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[77]  Yiming Wang,et al.  Automatic retinal image quality assessment and enhancement , 1999, Medical Imaging.

[78]  J. Forrester,et al.  Epidemiology of diabetic retinopathy and macular oedema: a systematic review , 2004, Eye.

[79]  Lalit Verma,et al.  Diabetic retinopathy: time for action. No complacency please! , 2002, Bulletin of the World Health Organization.

[80]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[81]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[82]  Qin Li,et al.  Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..

[83]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters , 2012, Journal of Medical Systems.

[84]  Peter F. Sharp,et al.  Automated microaneurysm detection , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[85]  P E Undrill,et al.  The application of wavelets to retinal image compression and its effect on automatic microaneurysm analysis. , 1998, Computer methods and programs in biomedicine.

[86]  Di Wu,et al.  On the adaptive detection of blood vessels in retinal images , 2006, IEEE Transactions on Biomedical Engineering.

[87]  B. Zinman,et al.  Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. , 2003, Diabetes care.

[88]  R. Hornero,et al.  Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy , 2003, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[89]  Alan Wee-Chung Liew,et al.  General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling , 2010, IEEE Transactions on Medical Imaging.

[90]  Hiroshi Fujita,et al.  Improvement of automatic hemorrhage detection methods using brightness correction on fundus images , 2008, SPIE Medical Imaging.

[91]  Mong-Li Lee,et al.  The role of domain knowledge in the detection of retinal hard exudates , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[92]  T. Teng,et al.  Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy , 2006, Medical and Biological Engineering and Computing.

[93]  U. Rajendra Acharya,et al.  Application of intuitionistic fuzzy histon segmentation for the automated detection of optic disc in digital fundus images , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[94]  Samuel C. Lee,et al.  Computer classification of nonproliferative diabetic retinopathy. , 2005, Archives of ophthalmology.

[95]  Gwénolé Quellec,et al.  Automated early detection of diabetic retinopathy. , 2010, Ophthalmology.

[96]  Marios S. Pattichis,et al.  Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection , 2010, IEEE Transactions on Medical Imaging.

[97]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[98]  Jacob Scharcanski,et al.  A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images , 2010, Comput. Medical Imaging Graph..

[99]  Jasjit S. Suri,et al.  Image modeling of the human eye , 2008 .

[100]  Kenneth W. Tobin,et al.  Detection of Anatomic Structures in Human Retinal Imagery , 2007, IEEE Transactions on Medical Imaging.

[101]  R. Reilly,et al.  Extraction of the optic disk boundary in digital fundus images , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[102]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[103]  Heinrich Niemann,et al.  Automated segmentation of the optic nerve head for diagnosis of glaucoma , 2005, Medical Image Anal..

[104]  Langis Gagnon,et al.  Procedure to detect anatomical structures in optical fundus images , 2001, SPIE Medical Imaging.

[105]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[106]  Peter F. Sharp,et al.  Detection of New Vessels on the Optic Disc Using Retinal Photographs , 2011, IEEE Transactions on Medical Imaging.

[107]  Evangelos Dermatas,et al.  Multi-scale retinal vessel segmentation using line tracking , 2010, Comput. Medical Imaging Graph..

[108]  Enrico Grisan,et al.  Detection of optic disc in retinal images by means of a geometrical model of vessel structure , 2004, IEEE Transactions on Medical Imaging.

[109]  Shankar M. Krishnan,et al.  Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter , 2002, IEEE Transactions on Biomedical Engineering.

[110]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[111]  Mirjana Huic,et al.  Vascular endothelial growth factor inhibitors (anti-VEGF) in the management of diabetic macular oedema: a systematic review , 2011, British Journal of Ophthalmology.

[112]  Gintautas Dzemyda,et al.  Automated Optic Nerve Disc Parameterization , 2008, Informatica.

[113]  N. Perumalsamy,et al.  Software for Reading and Grading Diabetic Retinopathy , 2007, Diabetes Care.

[114]  Marc Lalondey,et al.  Non-recursive paired tracking for vessel extraction from retinal images , 2000 .

[115]  U. Rajendra Acharya,et al.  Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach , 2013, Knowl. Based Syst..

[116]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[117]  Chanjira Sinthanayothin,et al.  Image analysis for automatic diagnosis of diabetic retinopathy. , 1999 .

[118]  Alireza Osareh,et al.  A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[119]  Mohammed Al-Rawi,et al.  Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images , 2007, Comput. Methods Programs Biomed..

[120]  Bunyarit Uyyanonvara,et al.  Automatic exudate detection for diabetic retinopathy screening , 2009 .

[121]  Qin Li,et al.  Hierarchical detection of red lesions in retinal images by multiscale correlation filtering , 2009, Medical Imaging.

[122]  J Cunha-Vaz,et al.  Diabetic Macular Edema , 1998 .

[123]  Lila Iznita Izhar,et al.  Analysis of retinal fundus images for grading of diabetic retinopathy severity , 2011, Medical & Biological Engineering & Computing.

[124]  Michael H. Goldbaum,et al.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels , 2003, IEEE Transactions on Medical Imaging.

[125]  A. Patz,et al.  Studies on retinal neovascularization. Friedenwald Lecture. , 1980, Investigative ophthalmology & visual science.

[126]  R. Klein,et al.  Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. , 1999, Ophthalmology.

[127]  András Hajdu,et al.  Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis , 2013, IEEE Transactions on Medical Imaging.

[128]  Karl-Hans Englmeier,et al.  Early detection of diabetes retinopathy by new algorithms for automatic recognition of vascular changes. , 2004, European journal of medical research.

[129]  M. Cree,et al.  An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice , 2006, Clinical & experimental optometry.

[130]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[131]  Kamesh Namuduri,et al.  A Decision Support Framework for Automated Screening of Diabetic Retinopathy , 2006, Int. J. Biomed. Imaging.

[132]  S.S. Lee,et al.  Screening of Diabetic Retinopathy - Automatic Segmentation of Optic Disc in Colour Fundus Images , 2006, The 2nd International Conference on Distributed Frameworks for Multimedia Applications.

[133]  M. Larsen,et al.  Automated detection of diabetic retinopathy in a fundus photographic screening population. , 2003, Investigative ophthalmology & visual science.

[134]  Roberto Hornero,et al.  Retinal image analysis based on mixture models to detect hard exudates , 2009, Medical Image Anal..

[135]  Ahmed Wasif Reza,et al.  A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy , 2009, Journal of Medical Systems.

[136]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[137]  Gary G. Yen,et al.  A Sorting System for Hierarchical Grading of Diabetic Fundus Images: A Preliminary Study , 2008, IEEE Transactions on Information Technology in Biomedicine.

[138]  K. Namuduri,et al.  Automated detection and classification of vascular abnormalities in diabetic retinopathy , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[139]  Petra Perner,et al.  Advances in Data Mining , 2002, Lecture Notes in Computer Science.

[140]  P F Sharp,et al.  An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. , 1996, Computers and biomedical research, an international journal.

[141]  Anil A. Bharath,et al.  Segmentation of blood vessels from red-free and fluorescein retinal images , 2007, Medical Image Anal..

[142]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[143]  J. Thiran,et al.  Identification of the optic disk boundary in retinal images using active contours , 1999 .

[144]  Badrinath Roysam,et al.  Integrated Analysis of Vascular and Nonvascular Changes From Color Retinal Fundus Image Sequences , 2007, IEEE Transactions on Biomedical Engineering.

[145]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[146]  Hamid Abrishami Moghaddam,et al.  A novel method for retinal vessel tracking using particle filters , 2013, Comput. Biol. Medicine.

[147]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[148]  Roberto Hornero,et al.  Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images , 2009, Medical Imaging.

[149]  M. Abràmoff,et al.  Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. , 2005, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[150]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[151]  U R Acharya,et al.  Decision support system for diabetic retinopathy using discrete wavelet transform. , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[152]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[153]  U. Rajendra Acharya,et al.  Automated identification of normal and diabetes heart rate signals using nonlinear measures , 2013, Comput. Biol. Medicine.

[154]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[155]  Mong-Li Lee,et al.  A piecewise Gaussian model for profiling and differentiating retinal vessels , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[156]  David Zhang,et al.  A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy , 2009, IEEE Transactions on Information Technology in Biomedicine.