Computer-aided diagnosis of diabetic retinopathy: A review

被引：295

作者：

Mookiah, Muthu Rama Krishnan ^{[1
]}

Acharya, U. Rajendra ^{[1
,2
]}

Chua, Chua Kuang ^{[1
]}

Lim, Choo Min ^{[1
]}

Ng, E. Y. K. ^{[3
]}

Laude, Augustinus ^{[4
]}

机构：

[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore

[2] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur 50603, Malaysia

[3] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore

[4] Tan Tock Seng Hosp, Natl Healthcare Grp Eye Inst, Singapore 308433, Singapore

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2013年 / 43卷 / 12期

关键词：

Retina; Retinopathy; Fundus imaging; Computer-aided diagnosis; Pattern classification; Image processing; OPTICAL COHERENCE TOMOGRAPHY; BLOOD-VESSEL SEGMENTATION; COLOR FUNDUS PHOTOGRAPHS; DECISION-SUPPORT-SYSTEM; RETINAL IMAGE-ANALYSIS; COTTON-WOOL SPOTS; AUTOMATED DETECTION; MACULAR EDEMA; MATCHED-FILTER; MATHEMATICAL MORPHOLOGY;

D O I：

10.1016/j.compbiomed.2013.10.007

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：2136 / 2155

页数：20

共 169 条

[61] Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study [J].

Hubbard, LD ;

Brothers, RJ ;

King, WN ;

Clegg, LX ;

Klein, R ;

Cooper, LS ;

Sharrett, AR ;

Davis, MD ;

Cai, JW .

OPHTHALMOLOGY, 1999, 106 (12) :2269-2280

[62]

Hunter A., 2002, NONLINEAR FILTERING

[63] An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice [J].

Jelinek, Herbert J. ;

Cree, Michael J. ;

Worsley, David ;

Luckie, Alan ;

Nixon, Peter .

CLINICAL AND EXPERIMENTAL OPTOMETRY, 2006, 89 (05) :299-305

[64] Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment [J].

Joshi, Gopal Datt ;

Sivaswamy, Jayanthi ;

Krishnadas, S. R. .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2011, 30 (06) :1192-1205

[65] A Decision Support Framework for Automated Screening of Diabetic Retinopathy [J].

Kahai, P. ;

Namuduri, K. R. ;

Thompson, H. .

INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING, 2006, 2006

[66]

Karim R, 2010, CLIN OPHTHALMOL, V4, P493

[67] SNAKES - ACTIVE CONTOUR MODELS [J].

KASS, M ;

WITKIN, A ;

TERZOPOULOS, D .

INTERNATIONAL JOURNAL OF COMPUTER VISION, 1987, 1 (04) :321-331

[68] Optical coherence tomographic patterns of diabetic macular edema [J].

Kim, Brian Y. ;

Smith, Scott D. ;

Kaiser, Peter K. .

AMERICAN JOURNAL OF OPHTHALMOLOGY, 2006, 142 (03) :405-412

[69] Automatic segmentation of age-related macular degeneration in retinal fundus images [J].

Koese, Cemal ;

Sevik, Ugur ;

Gencalioglu, Okyay .

COMPUTERS IN BIOLOGY AND MEDICINE, 2008, 38 (05) :611-619

[70] Wrappers for feature subset selection [J].

Kohavi, R ;

John, GH .

ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) :273-324

← 2 3 4 5 6 7 8 9 10 11 →