Image segmentation using voronoi polygons and MCMC, with application to muscle fibre images

Journal of Applied Statistics

Volumn 33, Issue 6, 2006, Pages 609-622

  Dryden, Ian L ^a   Farnoosh, Rahman ^b   Taylor, Charles C ^b  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Coloured tessellation; Markov chain Monte Carlo; Point pattern; Regularity; Reversible jump; Strauss process

Indexed keywords

EID: 33745884122     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664760600679825     Document Type: Article

References (18)

1. Baddeley, A. and Møller, J. (1989) Nearest neighbour Markov point processes and random sets. In International Statistical Review. (pp. 89-121).
2. Blackwell, P. and Møller, J. (2003) Bayesian inference for deformed tessellation models. In Advances in Applied Probability. (pp. 4-26).
3. Denison, D. and Holmes, C. (2001) Bayesian partitioning for estimating disease risk. In Biometrics. (pp. 143-149).
4. Dryden, I. and Faghihi, M. and Taylor, C. (1997) Procrustes shape analysis of spatial point patterns. In Journal of the Royal Statistical Society, Series B. (pp. 353-374).
5. Faghihi, M. and Dryden, I. and Taylor, C. (1999) Procrustes shape analysis of triangulations of a two-coloured point pattern. In Statistics and Computing. (pp. 43-53).
6. Farnoosh, R. (2000) Statistical analysis of nearly regular point patterns and images. In. University of Leeds. PhD thesis
7. Geyer, C. (1999) Likelihood inference for spatial point process. In Stochastic Geometry: Likelihood and Computation. (pp. 79-139). Chapman and Hall.
8. Geyer, C. and Møller, J. (1994) Simulation procedures and likelihood inference for spatial point processes. In Scandinavian Journal of Statistics. (pp. 359-373).
9. Green, P. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. In Biometrika. (pp. 711-732).
10. Green, P. and Sibson, R. (1978) Computing Dirichlet tessellations in the plane. In Computer Journal. (pp. 168-173).
11. Hastings, W. (1970) Monte Carlo sampling methods using Markov chains and their applications. In Biometrika. (pp. 97-109).
12. Johnson, M. and Polgar, J. and Weightman, D. and Appleton, D. (1973) Data on the distribution of fibre types in thirty-six human muscles - an autopsy study. In Journal of Neural Science. (pp. 111-129).
13. Klemencic, A. and Kovacic, S. and Pernus, F. (1998) Automated segmentation of muslce fibre images using active contour models. In Cytometry. (pp. 317-326).
14. Lexell, J. and Henriksson-Larsen, K. and Sjöström, M. (1983) Distribution of different fibre types in human skeletal muscles 2. A study of cross-section of whole m. vastus lateralis. In Acta. Physiol Scand. (pp. 115-122).
15. Møller, J. and Pettitt, A. and Berthelsen, K. and Reeves, R. (2004) An efficient Markov chain Monte Carlo method for distributions with intractable normalizing constants. In. Aalborg University. Research Report R-2004-02, Department of Mathematical Sciences
16. Møller, J. and Skare, O. (2001) Coloured Voronoi tessellations for Bayesian image analysis and reservoir modelling. In Statistical Modelling. (pp. 213-232).
17. Okabe, A. and Boots, B. and Sugihara, K. (1992) Spatial Tessellations. Chichester: Wiley.
18. Ripley, B. (1981) Spatial Statistics. New York: Wiley.