A two-phase decision support framework for the automatic screening of digital fundus images

Journal of Computational Science

Volumn 3, Issue 5, 2012, Pages 262-268

  Antal, Bálint ^a   Hajdu, András ^a   Maros Szabó, Zsuzsanna ^b   Török, Zsolt ^b   Csutak, Adrienne ^b   Peto, Tünde ^c  

^a UNIVERSITY OF DEBRECEN   (Hungary)

^b UNIVERSITY OF DEBRECEN   (Hungary)

^c MOORFIELDS EYE HOSPITAL   (United Kingdom)

Author keywords

Biomedical image processing; Medical decision making; Medical expert systems

Indexed keywords

AUTOMATED DETECTION; AUTOMATIC SCREENING; BIO-MEDICAL IMAGE PROCESSING; CLINICAL OBSERVATION; COMPUTATIONAL BURDEN; COMPUTATIONAL PERFORMANCE; DECISION SUPPORT FRAMEWORK; DIABETIC RETINOPATHY; DIGITAL FUNDUS IMAGES; MEDICAL DECISION MAKING; MEDICAL EXPERT SYSTEM; PRE-SCREENING ALGORITHMS; PRESENT STATUS; REGIONS OF INTEREST; SCREENING SYSTEM;

ALGORITHMS; EYE PROTECTION; FEATURE EXTRACTION; IMAGE PROCESSING;

DECISION SUPPORT SYSTEMS;

EID: 84865375633     PISSN: 18777503     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jocs.2012.01.001     Document Type: Article

References (24)

1. Klein R., Klein B.E.K., Moss S.E. Visual impairment in diabetes. Ophthalmology 1984, 91:1-9.
2. Causes of Vision Impairment. http://www.lighthouse.org/research/statistics-on-vision-impairment/causes/.
3. Vanderbilt eye institute. http://www.vanderbilthealth.com/eyeinstitute/23499.
4. Eyepacs. http://www.eyepacs.org/about.do.
5. English national screening programme for diabetic retinopathy. http://www.retinalscreening.nhs.uk/pages/.
6. Eyecheck. http://www.eyecheck.nl.
7. Philip S., Fleming A.D., Goatman K.A., Fonseca S., Mcnamee P., Scotland G.S., Prescott G.J., Sharp P.F., Olson J.A. The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme. British Journal of Ophthalmology 2007, 91(11):1512-1517.
8. Winder R., Morrow P., McRitchie I., Bailie J., Hart P. Algorithms for digital image processing in diabetic retinopathy. Computerized Medical Imaging and Graphics 2009, 33(8):608-622.
9. Abramoff M., Niemeijer M., Suttorp-Schulten M., Viergever M.A., Russel S.R., van Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 2008, 31(February):193-198.
10. Chorley M.J., Walker D.W. Performance analysis of a hybrid mpi/openmp application on multi-core clusters. Journal of Computational Science 2010, 1(3):168-174.
11. Bader M., Mehl M., Rüde U., Wellein G. Simulation software for supercomputers. Journal of Computational Science 2011, 2(2):93-94.
12. Niemeijer M., Abrámoff M.D., van Ginneken B. Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Medical Image Analysis 2006, 10:888-898.
13. Staal J., Abramoff M., Niemeijer M., Viergever M., van Ginneken B. Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 2004, 23:501-509.
14. Kozak I., Sample P., Hao J., Freeman W.R., Weinreb R.N., Lee T., Goldbaum M.H. Machine learning classifiers detect subtle field defects in eyes of HIV individuals. Transactions of the American Ophthalmological Society 2007, 105:111-120.
15. Meier J., Bock R., Michelson G., Nyúl L., Hornegger J. Effects of preprocessing eye fundus images on appearance based glaucoma classification. Lecture Notes in Computer Science 2007, 4673:165-173.
16. Sopharak A., Nwe K.T., Moe Y.A., Dailey M.N., Uyyanonvara B. Automatic exudate detection with a naive bayes classifier. The 2008 International Conference on Embedded Systems and Intelligent Technology 2008.
17. A.A.A. Youssif, A.Z. Ghalwash, A.S. Ghoneim, Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation, in: Proc. Cairo International Biomedical Engineering Conference, 2006.
18. Bellman C., Neveu M.M., Scholl H.P.N., Hogg C.R., Rath P.R., Jenkins S., Bird A.C., Holder G.E. Localized retinal electrophysiological and fundus autofluorescence imaging abnormalities in maternal inherited diabetes and deafness. Investigative Ophthalmology & Visual Science 2004, 45(July):2355-2360.
19. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco. 2nd edition.
20. Staal J., Abramoff M.D., Niemeijer M., Viergever M.A., van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 2004, 23:501-509.
21. T.S. Petsatodis, A. Diamantis, G. P. Syrcos, A complete algorithm for automatic human recognition based on retina vascular network characteristics, in: Era1 International Scientific Conference, Peloponnese, Greece, 2004.
22. Kauppi T., Kalesnykiene V., Kämäräinen J., Lensu L., Sorri I., Raninen A., Voutilainen R., Uusitalo H., Kälviäinen H., Pietilä J. Diaretdb1 diabetic retinopathy database and evaluation protocol. Proc. of the 11th Conf. on Medical Image Understanding and Analysis (MIUA2007) 2007, 61-65.
23. Harding S., Greenwood R., Aldington S., Gibson J., Owens D., Taylor R., Kohner E., Scanlon P., Leese G. Grading and disease management in national screening for diabetic retinopathy in England and Wales. Diabetic Medicine 2003, 20(December):965-971.
24. Fleming A.D., Philip S., Goatman K.A. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Transactions on Medical Imaging 2006, 25(9):1223-1232.