Microaneurysm detection with radon transform-based classification on retina images

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 5939-5942

  Giancardo, L ^a   Meriaudeau, F ^a   Karnowski, T P ^b   Li, Y ^c   Tobin, K W ^b   Chaum, E ^d  

^a UNIVERSITÉ DE BOURGOGNE   (France)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^d Health Science Center ^*

Author keywords

[No Author keywords available]

Indexed keywords

DIABETIC RETINOPATHY SCREENING; FALSE POSITIVE; IMAGE PREPROCESSING; IMAGING COMMUNITY; KEY ELEMENTS; MICROANEURYSMS; MORPHOLOGICAL FEATURES; NOVEL APPLICATIONS; PUBLIC DATASET; RADON TRANSFORM; RETINA CAMERA; SEGMENTATION TECHNIQUES;

EYE PROTECTION; IMAGE SEGMENTATION; MATHEMATICAL TRANSFORMATIONS;

MEDICAL IMAGING;

ALGORITHM; ANEURYSM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIABETIC RETINOPATHY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; RETINA ARTERY; RETINOSCOPY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANEURYSM; DIABETIC RETINOPATHY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RETINAL ARTERY; RETINOSCOPY; SENSITIVITY AND SPECIFICITY;

EID: 84055189171     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6091562     Document Type: Conference Paper

References (10)

1. Centers for Disease Control and Prevention, "National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the united states," U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Tech. Rep., 2011.
2. C. E. Baudoin, B. J. Lay, and J. C. Klein, "Automatic detection of microaneurysms in diabetic fluorescein angiography." Rev Epidemiol Sante Publique, vol. 32, no. 3-4, pp. 254-261, 1984.
3. T. Spencer, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus." Comput Biomed Res, vol. 29, no. 4, pp. 284-302, Aug 1996.
4. J. A. Frame, P. E. Undrill, M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms," Computers in Biology and Medicine, vol. 28, pp. 225-238, 1998.
5. M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, "Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 185-195, Jan. 2010.
6. B. Antal, I. Lazar, and A. Hajdu, "An ensemble-based system for microaneurysm detection," University of Debrecen, Hungary, Tech. Rep., 2010. [Online]. Available: http://roc.healthcare.uiowa.edu/results/ documentation/drscreen.pdf
7. M. Niemeijer, B. van Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abramoff, "Automatic detection of red lesions in digital color fundus photographs," IEEE Transactions on Medical Imaging, vol. 24, no. 5, pp. 584-592, 2005.
8. L. Giancardo, F. Meriaudeau, T. Karnowski, K. Tobin Jr, Y. Li, and E. Chaum, "Microaneurysms detection with the radon cliff operator in retinal fundus images," in Proceedings of SPIE, vol. 7623, 2010, p. 76230U.
9. L. Giancardo, F. Meriaudeau, T. P. Karnowski, E. Chaum, and K. Tobin, Quality Assessment of Retinal Fundus Images using ELVD. IN-TECH, 2010, ch. New Developments in Biomedical Engineering, pp. 201-224.
10. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001. [Online]. Available: http://www.csie.ntu.edu.tw/-cjlin/libsvm