Authors:
Clément Beitone
1
;
Christophe Tilmant
1
and
Frederic Chausse
2
Affiliations:
1
Clermont Université, Univ. Blaise Pascal, CNRS and UMR 6602, France
;
2
Clermont Université, Univ. d’Auvergne, CNRS and UMR 6602, France
Keyword(s):
Fully Automatic Segmentation, Deformable Model, MRI, Weibull Model, Monogenic Signal.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Medical Image Applications
;
Segmentation and Grouping
Abstract:
This article presents a fully automatic left ventricle (LV) segmentation method on MR images by means of an implicit deformable model (Level Set) in a variational context. For these parametrizations, the degrees of freedom are: initialization and functional energy. The first is often delegated to the practician. To avoid this human intervention, we present an automatic initialisation method based on the Hough transform exploiting spatio-temporal information. Generally, energetic functionals integrate edges, regions and shape terms. We propose to bundle an edge-based energy computed by feature asymmetry on the monogenic signal, a regionbased energy capitalizing on image statistics (Weibull model) and a shape-based energy constrained by the
myocardium thickness. The presence of multiple tissues implies data non-stationarity. To best estimate distribution parameters over the regions and regarding anatomy, we propose a deformable model maximizing locally and globally the log-likelihood.
Finally, we evaluate our method on MICCAI 09 Challenge data.
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