title: MAP MRF joint segmentation and registration of medical images.
abstract: The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. In this paper, we aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a maximum a posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.
authors.affiliation: Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford, UK. wyatt@robots.ox.ac.uk
authors.forename: Paul P
authors.initials: PP
authors.lastname: Wyatt
authors.forename: J Alison
authors.initials: JA
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D051379:Mice; D019047:Phantoms, Imaging