A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms

Computers in Biology and Medicine

Volumn 28, Issue 3, 1998, Pages 225-238

  Frame, Allan J ^a   Undrill, Peter E ^a   Cree, Michael J ^a   Olson, John A ^a   McHardy, Kenneth C ^b   Sharp, Peter F ^a   Forrester, John V ^a  

^a UNIVERSITY OF ABERDEEN   (United Kingdom)

^b ABERDEEN ROYAL INFIRMARY   (United Kingdom)

Author keywords

Computer aided diagnosis; Linear discriminant analysis; Neural networks; Ophthalmology; Rule based system

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; DIABETIC RETINOPATHY; DISCRIMINANT ANALYSIS; HUMAN; MICROANEURYSM; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETINA FLUORESCEIN ANGIOGRAPHY;

ANEURYSM; ARTIFICIAL INTELLIGENCE; DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; DISCRIMINANT ANALYSIS; FLUORESCEIN ANGIOGRAPHY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RETINAL VESSELS; ROC CURVE; SENSITIVITY AND SPECIFICITY; TIME FACTORS;

EID: 0031694040     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0010-4825(98)00011-0     Document Type: Article

References (21)

1. I.M. Ghafour, D. Allan, W.S. Foulds, Common causes of blindness and visual handicap in the west of Scotland, Br. J. Ophthalmol. 67 (1983) 209.
2. Diabetic Retinopathy Study Research Group, A modification of the Airlie House classification of diabetic retinopathy, Investigative Ophthalmology and Visual Science 21 (1981) 107.
3. Early Treatment of Diabetic Retinopathy Study Group, Grading diabetic retinopathy from stereoscopic color fundus photographs: an extension of the Airlie House classification, Ophthalmology 98 (1991) 786.
4. R. Klein, S.M. Meuer, S.E. Moss, B.E.K. Klein, The relationship of retinal microaneurysm counts to the four year progression of diabetic retinopathy, Archives of Ophthalmology 107 (1989) 1780.
5. E.M. Kohner, M. Sleightholm, KROC collaborative study group, does microaneurysm count reflect the severity of early diabetic retinopathy, Ophthalmology 93 (1986) 586.
6. C. Baudoin, F. Maneschi, G. Quentel, G. Soubrane, T. Hayes, G. Jones, G. Coscas, E.M. Kohner, Quantitative evaluation of fluorescein angiograms: microaneurysm counts, Diabetes 32(Suppl. 2) (1983) 8.
7. R.C. Gonzalez, R.C. Woods, Digital Image Processing, Addison-Wesley, Reading, MA, 1992.
8. T. Spencer, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester, An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus, Computers and Biomedical Research 29 (1996) 284.
9. M.J. Cree, J.A. Olson, K.C. McHardy, J.V. Forrester, P.F. Sharp, Automated microaneurysm detection, IEEE International Conference on Image Processing 3 (1996) 699.
10. M.J. Cree, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester, A fully automated comparative microaneurysm digital detection system, Eye, 1998, in press.
11. C. Metz, ROC methodology in radiologie imaging, Investigative Radiology 21 (1986) 720.
12. D. Keating, E. Mutlukan, A. Evans, J. McGarvie, B. E. Damato, A back propagation neural network for the classification of visual field data, Physics in Medicine and Biology 38 (1993) 1263.
13. J. Caprioli, Discrimination between normal and glaucomatous eyes, Investigative Ophthalmology and Visual Science 33 (1992) 153.
14. H. Hirsbrunner, F. Fankhauser, A. Jenni, A. Funkhouser, Evaluating a perimetric expert system: experience with Octosmart, Graefe's Archive for Clinical and Experimental Ophthalmology 228 (1990) 237.
15. J.A. Swets, R.M. Pickett, The evaluation of diagnostic systems: methods from signal detection theory, New York, Academic Press, 1982.
16. D.P. Chakraborty, Observer performance methodology in medical imaging, Tutorial Notes, SPIE Medical Imaging '96, 1996.
17. W. Press, B. Flannery, S. Teukolsky, W. Vetterling, Numerical recipes in C, Cambridge University Press, 1992.
18. C.E. Metz, Basic priciples of ROC analysis, Seminars in Nuclear Medicine 8 (1978) 283.
19. J.M.O. Mitchell, Classical Statistical Methods, in: D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.), Machine Learning, Neural and Statistical Classification, Ellis Horwood, London, 1994.
20. T. Kohonen, J. Kangas, J. Laaksonen, K. Torkkola, LVQ_PAK: A program package for the correct application of learning vector quantization algorithms, Proceedings of the IEEE International Joint Conference on Neural Networks, 1992.
21. J. Henery, Review of previous empirical comparisons, in: D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.), Machine Learning, Neural and Statistical Classification, Ellis Horwood, New York, 1994.