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Segmentation of echocardiographic images with Markov random fields

Abstract : 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.
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Submitted on : Wednesday, May 24, 2006 - 2:51:44 PM
Last modification on : Sunday, March 20, 2022 - 9:40:02 PM
Long-term archiving on: : Monday, April 5, 2010 - 12:07:16 AM


  • HAL Id : inria-00074251, version 1



Isabelle Herlin, Dominique Béréziat, Gérard Giraudon, Catherine Nguyen, Christine Graffigne. Segmentation of echocardiographic images with Markov random fields. [Research Report] RR-2424, INRIA. 1994. ⟨inria-00074251⟩



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