Literature DB >> 14507751

Automated identification of diabetic retinal exudates in digital colour images.

A Osareh1, M Mirmehdi, B Thomas, R Markham.   

Abstract

AIM: To identify retinal exudates automatically from colour retinal images.
METHODS: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated.
RESULTS: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification.
CONCLUSIONS: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.

Entities:  

Mesh:

Year:  2003        PMID: 14507751      PMCID: PMC1920779          DOI: 10.1136/bjo.87.10.1220

Source DB:  PubMed          Journal:  Br J Ophthalmol        ISSN: 0007-1161            Impact factor:   4.638


  3 in total

1.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images.

Authors:  C Sinthanayothin; J F Boyce; H L Cook; T H Williamson
Journal:  Br J Ophthalmol       Date:  1999-08       Impact factor: 4.638

Review 2.  Assessing test accuracy and its clinical consequences: a primer for receiver operating characteristic curve analysis.

Authors:  A R Henderson
Journal:  Ann Clin Biochem       Date:  1993-11       Impact factor: 2.057

3.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

Authors:  G G Gardner; D Keating; T H Williamson; A T Elliott
Journal:  Br J Ophthalmol       Date:  1996-11       Impact factor: 4.638

  3 in total
  13 in total

Review 1.  Retinal imaging and image analysis.

Authors:  Michael D Abràmoff; Mona K Garvin; Milan Sonka
Journal:  IEEE Rev Biomed Eng       Date:  2010

2.  Neovascularization detection in diabetic retinopathy from fluorescein angiograms.

Authors:  Benjamin Béouche-Hélias; David Helbert; Cynthia de Malézieu; Nicolas Leveziel; Christine Fernandez-Maloigne
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-16

3.  Multiscale AM-FM methods for diabetic retinopathy lesion detection.

Authors:  Carla Agurto; Victor Murray; Eduardo Barriga; Sergio Murillo; Marios Pattichis; Herbert Davis; Stephen Russell; Michael Abramoff; Peter Soliz
Journal:  IEEE Trans Med Imaging       Date:  2010-02       Impact factor: 10.048

4.  Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space.

Authors:  Buket Toptaş; Murat Toptaş; Davut Hanbay
Journal:  J Digit Imaging       Date:  2022-01-11       Impact factor: 4.056

Review 5.  Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends.

Authors:  Baoxin Li; Helen K Li
Journal:  Curr Diab Rep       Date:  2013-08       Impact factor: 4.810

6.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

Authors:  Meindert Niemeijer; Bram van Ginneken; Stephen R Russell; Maria S A Suttorp-Schulten; Michael D Abràmoff
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-05       Impact factor: 4.799

7.  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

8.  An automated tracking approach for extraction of retinal vasculature in fundus images.

Authors:  Alireza Osareh; Bita Shadgar
Journal:  J Ophthalmic Vis Res       Date:  2010-01

9.  The reading of components of diabetic retinopathy: an evolutionary approach for filtering normal digital fundus imaging in screening and population based studies.

Authors:  Hongying Lilian Tang; Jonathan Goh; Tunde Peto; Bingo Wing-Kuen Ling; Lutfiah Ismail Al Turk; Yin Hu; Su Wang; George Michael Saleh
Journal:  PLoS One       Date:  2013-07-01       Impact factor: 3.240

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

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