Literature DB >> 8444365

Automated detection and quantification of retinal exudates.

R Phillips1, J Forrester, P Sharp.   

Abstract

Retinal exudates are a common manifestation of vascular damage in a variety of retinal diseases. We have used computerized image analysis to detect and measure the area of exudates from digitized colour fundus slides of patients with diabetic retinopathy and have assessed the repeatability, reproducibility, and accuracy of the technique. The analysis was entirely independent of the operator apart from choice of the region to be analysed. The coefficient of variation for repeatability was between 3% for large areas of exudate and 17% for small areas of exudate. The reproducibility was also within this range. Sensitivity was between 61 and 100% (mean 87%). False-positives were observed in 5 of 30 regions analysed, and these could have been eliminated by using more stringent criteria for selection of images for analysis. Time taken for the analysis was approximately 3 min.

Entities:  

Mesh:

Year:  1993        PMID: 8444365     DOI: 10.1007/bf00920219

Source DB:  PubMed          Journal:  Graefes Arch Clin Exp Ophthalmol        ISSN: 0721-832X            Impact factor:   3.117


  5 in total

1.  Confocal scanning laser ophthalmoscope.

Authors:  R H Webb; G W Hughes; F C Delori
Journal:  Appl Opt       Date:  1987-04-15       Impact factor: 1.980

2.  Detection and quantification of hyperfluorescent leakage by computer analysis of fundus fluorescein angiograms.

Authors:  R P Phillips; P G Ross; M Tyska; P F Sharp; J V Forrester
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  1991       Impact factor: 3.117

3.  Image analysis of fundus photographs. The detection and measurement of exudates associated with diabetic retinopathy.

Authors:  N P Ward; S Tomlinson; C J Taylor
Journal:  Ophthalmology       Date:  1989-01       Impact factor: 12.079

4.  Automated detection and quantification of microaneurysms in fluorescein angiograms.

Authors:  T Spencer; R P Phillips; P F Sharp; J V Forrester
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  1992       Impact factor: 3.117

5.  Diabetic retinopathy study. Report Number 6. Design, methods, and baseline results. Report Number 7. A modification of the Airlie House classification of diabetic retinopathy. Prepared by the Diabetic Retinopathy.

Authors: 
Journal:  Invest Ophthalmol Vis Sci       Date:  1981-07       Impact factor: 4.799

  5 in total
  10 in total

1.  Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation.

Authors:  Ahmed Wasif Reza; C Eswaran; Kaharudin Dimyati
Journal:  J Med Syst       Date:  2010-01-29       Impact factor: 4.460

2.  Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds.

Authors:  Ahmed Wasif Reza; C Eswaran; Subhas Hati
Journal:  J Med Syst       Date:  2009-02       Impact factor: 4.460

3.  Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images.

Authors:  Karthikeyan Ganesan; Roshan Joy Martis; U Rajendra Acharya; Chua Kuang Chua; Lim Choo Min; E Y K Ng; Augustinus Laude
Journal:  Med Biol Eng Comput       Date:  2014-06-24       Impact factor: 2.602

4.  Automated identification of exudates and optic disc based on inverse surface thresholding.

Authors:  Haniza Yazid; Hamzah Arof; Hazlita Mohd Isa
Journal:  J Med Syst       Date:  2011-02-12       Impact factor: 4.460

5.  Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network.

Authors:  Rui Zheng; Lei Liu; Shulin Zhang; Chun Zheng; Filiz Bunyak; Ronald Xu; Bin Li; Mingzhai Sun
Journal:  Biomed Opt Express       Date:  2018-09-14       Impact factor: 3.732

6.  Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

Authors:  T Jaya; J Dheeba; N Albert Singh
Journal:  J Digit Imaging       Date:  2015-12       Impact factor: 4.056

Review 7.  Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review.

Authors:  Oliver Faust; Rajendra Acharya U; E Y K Ng; Kwan-Hoong Ng; Jasjit S Suri
Journal:  J Med Syst       Date:  2010-04-06       Impact factor: 4.460

8.  Automated identification of diabetic retinopathy stages using digital fundus images.

Authors:  Jagadish Nayak; P Subbanna Bhat; Rajendra Acharya; C M Lim; Manjunath Kagathi
Journal:  J Med Syst       Date:  2008-04       Impact factor: 4.460

9.  Fundus imaging in patients with cataract: role for a variable wavelength scanning laser ophthalmoscope.

Authors:  J N Kirkpatrick; A Manivannan; A K Gupta; J Hipwell; J V Forrester; P F Sharp
Journal:  Br J Ophthalmol       Date:  1995-10       Impact factor: 4.638

10.  Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

Authors:  Akara Sopharak; Bunyarit Uyyanonvara; Sarah Barman
Journal:  Sensors (Basel)       Date:  2009-03-24       Impact factor: 3.576

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.