Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

被引：259

作者：

Sopharak, Akara ^{[1
]}

Uyyanonvara, Bunyarit ^{[1
]}

Barman, Sarah ^{[2
]}

Williamson, Thomas H. ^{[3
]}

机构：

[1] Thammasat Univ, SIIT, Dept Informat Technol, Muang 12000, Pathumthani, Thailand

[2] Kingston Univ, Fac Comp Informat Syst & Math, Kingston upon Thames KT1 2EE, Surrey, England

[3] St Thomas Hosp, Dept Ophthalmol, London SE1 7EH, England

来源：

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2008年 / 32卷 / 08期

关键词：

Diabetic retinopathy; Exudates; Retinal image; Non-dilated retinal images; Morphology;

D O I：

10.1016/j.compmedimag.2008.08.009

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Diabetic retinopathy is a complication of diabetes that is caused by changes in the blood vessels of the retina. The symptoms call blur or distort the patient's vision and are a main cause of blindness. Exudates are one of the primary signs of diabetic retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a chemical Solution which takes time and affects patients. This paper investigates and proposes a set of optimally adjusted morphological operators 10 be used for exudate detection on diabetic retinopathy patients' non-dilated, pupil and low-contrast images. These automatically detected exudates are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. The results are successful and the sensitivity and specificity for Our exudate detection is 80% and 99.5%, respectively. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：720 / 727

页数：8

共 19 条

[1]

[Anonymous], 1991, Ophthalmology, V98, P786

[2] Comparison of time-domain OCT and fundus photographic assessments of retinal thickening in eyes with diabetic macular edema [J].

Davis, Matthew D. ;

Bressler, Susan B. ;

Aiello, Lloyd Paul ;

Bressler, Neil M. ;

Browning, David J. ;

Flaxel, Christina J. ;

Fong, Donald S. ;

Foster, William J. ;

Glassman, Adam R. ;

Hartnett, Mary Elizabeth R. ;

Kollman, Craig ;

Li, Helen K. ;

Qin, Haijing ;

Scott, Ingrid U. .

INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2008, 49 (05) :1745-1752

[3] Screening for diabetic retinopathy using computer based image analysis and statistical classification [J].

Ege, BM ;

Hejlesen, OK ;

Larsen, OV ;

Moller, K ;

Jennings, B ;

Kerr, D ;

Cavan, DA .

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2000, 62 (03) :165-175

[4] Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool [J].

Gardner, GG ;

Keating, D ;

Williamson, TH ;

Elliott, AT .

BRITISH JOURNAL OF OPHTHALMOLOGY, 1996, 80 (11) :940-944

[5]

GONZALES RC, 1993, DIGITAL IMAGE PROCES, P75

[6] Quantitative analysis of retinopathy in type 2 diabetes: identification of prognostic parameters for developing visual loss secondary to diabetic maculopathy [J].

Hove, MN ;

Kristensen, JK ;

Lauritzen, T ;

Bek, T .

ACTA OPHTHALMOLOGICA SCANDINAVICA, 2004, 82 (06) :679-685

[7]

Hsu W, 2001, PROC CVPR IEEE, P246

[8]

JAVITT JC, 1991, OPHTHALMOLOGY, V98, P1565

[9]

Kanski J., 1997, DIABETIC RETINOPATHY

[10] A Bayesian network based sequential inference for diagnosis of diseases from retinal images [J].

Mitra, SK ;

Lee, TW ;

Goldbaum, M .

PATTERN RECOGNITION LETTERS, 2005, 26 (04) :459-470

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