Maximum likelihood estimation of vessel parameters from scale space analysis

Image and Vision Computing

Volumn 28, Issue 1, 2010, Pages 55-63

  Ng, J ^a   Clay, S T ^a   Barman, S A ^c   Fielder, A R ^b   Moseley, M J ^b   Parker, K H ^a   Paterson, C ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b CITY UNIVERSITY   (United Kingdom)

^c KINGSTON UNIVERSITY   (United Kingdom)

Author keywords

Automatic segmentation; Maximum likelihood; Retinal blood vessels; Scale space

Indexed keywords

BLOOD; BLOOD VESSELS; OPHTHALMOLOGY; PARAMETER ESTIMATION;

AUTOMATIC SEGMENTATION; ESTIMATE MODEL; ESTIMATED PARAMETER; EXISTING SYSTEMS; FILTER RESPONSE; GAUSSIANS; GROUND TRUTH; MAXIMUM LIKELIHOOD ESTIMATOR; MODEL BASED APPROACH; NOISE MODELS; PARAMETRIC MODELS; REGION GROWING; RETINAL BLOOD VESSELS; RETINAL IMAGE; SCALE SPACE; SCALE SPACES; SCALE-SPACE ANALYSIS; TARGET FEATURE; THRESHOLDING METHODS; VESSEL TRACKING;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 70449526394     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2009.04.019     Document Type: Article

References (23)

1. Can A., Shen H., Turner J., Tanenbaum H., and Roysam B. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Transactions on Information Technology in Biomedicine 3 (1999) 125-138
2. Chaudhuri S., Chatterjee S., Katz N., Nelson M., and Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging 8 (1989) 263-269
3. O. Chutatape, L. Zheng, S. Krishnan, Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, in: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, 1998, pp. 3144-3149.
4. Florack L., ter Haar Romeny B., Koenderink J., and Viergever M. Scale and the differential structure of images. Image and Vision Computing 10 (1992) 376-388
5. E. Grisan, A. Pesce, A. Giani, M. Foracchia, A. Ruggeri, A new tracking system for the robust extraction of retinal vessel structure, in: Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, USA, pp. 1620-1623.
6. Hoover A., Kouznetsova V., and Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19 (2000) 203-210
7. Jiang X., and Mojon D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003) 131-137
8. Lindeberg T. Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30 (1998) 117-154
9. Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision 30 (1998) 79-116
10. Martinez-Perez M., Hughes A., Stanton A., Thom S., Chapman N., Bharath A., and Parker K. Retinal vascular tree morphology: a semiautomatic quantification. IEEE Transactions on Biomedical Engineering 49 (2002) 912-917
11. Miles F., and Nuttall A. Matched filter estimation of serial blood vessel diameters from video images. IEEE Transactions on Medical Imaging 12 (1993) 147-152
12. Sinthanayothin C., Boyce J., Cook H., and Williamson T. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology 83 (1999) 902-910
13. Staal J., Abramoff M., Niemeijer M., Vierverger M., and van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23 (2004) 501-509
14. STARE, STARE project homepage, 2005. Available from: .
15. Steger C. An unbiased detector of curvilinear structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 113-125
16. Walter T., and Klein J.-C. Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. In: Crespo J., Maojo V., and Martin F. (Eds). Medical Data Analysis (2001), Springer 282-287
17. L. Wang, A. Bhalerao, R. Wilson, Robust modelling of local image structures and its application to medical imagery, in: Proceedings of the International Conference on Pattern Recognition, Cambridge, vol. III, 2004, p. 534.
18. Zana F., and Klein J. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing 10 (2001) 1010-1019
19. Friedenwald J.S. Diabetic retinopathy. American Journal of Ophthalmology 33 (1950) 1187-1199
20. Unsworth A.C. Retrolental fibroplasia or ophthalmic dysplasia of premature infants. Transactions of the American Ophthalmology Society 47 (1949) 738-771
21. Hart W., Goldbaum M., Ot B., Kube E., and Nelson M. Measurement and classification of retinal vascular tortuosity. International Journal of Medical Informatics 53 2-3 (1999) 239-252
22. Wilson C.M., Cocker K.D., Moseley M.J., Clay S.T., Paterson C., Schu-lenburg W.E., Mills M.D., Ells A.L., Parker K.H., Quinn G.E., Fielder A.R., and Ng J. Computerized analysis of retinal vessel width and tortuosity in premature infants. Investigative Ophthalmology and Visual Science 49 (2008) 3577-3585
23. Wilson C.M., Cocker K.D., Moseley M.J., Ng J., and Fielder A.R. Vessel parameters of the developing preterm retina. Association for Research in Vision and Ophthalmology (2008)