Segmentation of echocardiographic images with Markov random fields
Résumé
The aim of this work is to track specific anatomical structures in temporal sequences of echocardiographic images. Ultrasound images are available in two broad data types: raw or video data. Different stochastic processes using different kind of information are compared on the basis of these two data types. We explain the selection of a particular model w.r.t. the type of data, and describe the relevant properties that must be taken into account to obtain the best possible results. The models are expressed within a Markov random field framework and we also discuss parameter estimation and energy minimization for the different models.