A Bayesian approach to inferring vascular tree structure from 2D imagery

Author

Thonnes, Elke ; Bhalerao, Abhir ; Kendall, Wilfrid ; Wilson, Roland

Author_Institution

Dept. of Comput. Sci., Warwick Univ., Coventry, UK

Volume

2

fYear

2002

fDate

2002

Abstract

We describe a method for inferring tree-like vascular structures from 2D imagery. A Markov chain Monte Carlo (MCMC) algorithm is employed to sample from the posterior distribution given local feature estimates, derived from likelihood maximisation for a Gaussian intensity profile. A multiresolution scheme, in which coarse scale estimates are used to initialise the algorithm for finer scales, has been implemented and used to model retinal images. Results are presented to show the effectiveness of the method.

Keywords

Bayes methods; Gaussian distribution; Markov processes; Monte Carlo methods; biomedical optical imaging; blood vessels; eye; image resolution; medical image processing; parameter estimation; trees (mathematics); 2D imagery; Bayesian approach; Gaussian intensity profile; Markov chain Monte Carlo algorithm; likelihood maximisation; local feature estimation; posterior distribution; random-walk tree; retinal images; vascular tree structure; Bayesian methods; Biomedical imaging; Image analysis; Image resolution; Inference algorithms; Monte Carlo methods; Retina; Spatial resolution; Surgery; Tree data structures;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1040106

Filename

1040106