Automated diagnosis of referable maculopathy in diabetic retinopathy screening

This paper introduces an algorithm for the automated diagnosis of referable maculopathy in retinal images for diabetic retinopathy screening. Referable maculopathy is a potentially sight-threatening condition requiring immediate referral to an ophthalmologist from the screening service, and therefore accurate referral is extremely important. The algorithm uses a pipeline of detection and filtering of “peak points” with strong local contrast, segmentation of candidate lesions, extraction of features and classification by a multilayer perceptron. The optic nerve head and fovea are detected, so that the macula region can be identified and scanned. The algorithm is assessed against a reference standard database drawn from the Birmingham City Hospital (UK) diabetic retinopathy screening programme, against two possible modes of use: independent screening, and pre-filtering to reduce human screener workload.

[1]  Andrew Hunter,et al.  An automated retinal image quality grading algorithm , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  J. Olson,et al.  The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy , 2009, British Journal of Ophthalmology.

[3]  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.

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

[5]  P. Scanlon,et al.  The effectiveness of screening for diabetic retinopathy by digital imaging photography and technician ophthalmoscopy , 2003, Diabetic medicine : a journal of the British Diabetic Association.

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

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

[8]  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.

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

[10]  Majid Mirmehdi,et al.  Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks , 2001 .

[11]  P F Sharp,et al.  Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland , 2007, British Journal of Ophthalmology.

[12]  Andrew Hunter,et al.  A robust lesion boundary segmentation algorithm using level set methods , 2009 .

[13]  D. DeMets,et al.  The Wisconsin epidemiologic study of diabetic retinopathy. II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. , 1984, Archives of ophthalmology.

[14]  Oddbjørn Engvold,et al.  Microphotometry of the blood column and the light streak on retinal vessels in fundus photographs , 1986 .

[15]  Andrew Hunter,et al.  Quantification of Diabetic Retinopathy using Neural Networks and Sensitivity Analysis , 2000, ANNIMAB.

[16]  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.

[17]  Geir E. Øien,et al.  Diabetic retinopathy : Automatic detection of earlysymptoms from retinal , 1995 .

[18]  Michael Larsen,et al.  Assessment of Automated Screening for Treatment-Requiring Diabetic Retinopathy , 2007, Current eye research.

[19]  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.

[20]  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.