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

Authors:
Frédéric J. P. Richard
Affiliations:
Department of Mathematics and Computer Science, Laboratory MAP5 (UMR CNRS 8145), University Paris 5, 45, rue des Saints Pères, 75270 Paris Cedex 06, France
Venue:
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Year:
2005

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Abstract

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.