A comparative study of markovian and variational image-matching techniques in application to mammograms

In this paper, we focus our interest on the image-matching problem. This major problem in Image Processing has received a considerable attention in the last decade. However, contrarily to other image-processing problems such as image restoration, the image-matching problem have been mainly tackled using a single approach based on variational principles. In this paper, our motivation is to investigate the feasibility of another famous image-processing approach based on Markov random fields (MRF). For that, we propose a discrete and stochastic image-matching framework which is equivalent to an usual variational one and suitable for an MRF-based approach. In this framework, we describe multigrid implementations of two algorithms: an iterated conditional modes (ICM) and a simulated annealing. We apply these algorithms for the registration of mammograms and compare their performances to those of an usual variational algorithm. We come to the conclusion that MRF-based techniques are optimization techniques which are relevant for the mammogram application. We also point out some of their specific properties and mention interesting perspectives offered by the markovian approach.