Fully Automatic Deformable Model Integrating Edge, Texture and Shape - Application to Cardiac Images Segmentation

Clément Beitone, Christophe Tilmant, Frederic Chausse

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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Paper Citation


in Harvard Style

Beitone C., Tilmant C. and Chausse F. (2015). Fully Automatic Deformable Model Integrating Edge, Texture and Shape - Application to Cardiac Images Segmentation . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 517-522. DOI: 10.5220/0005304005170522


in Bibtex Style

@conference{visapp15,
author={Clément Beitone and Christophe Tilmant and Frederic Chausse},
title={Fully Automatic Deformable Model Integrating Edge, Texture and Shape - Application to Cardiac Images Segmentation},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={517-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005304005170522},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Fully Automatic Deformable Model Integrating Edge, Texture and Shape - Application to Cardiac Images Segmentation
SN - 978-989-758-089-5
AU - Beitone C.
AU - Tilmant C.
AU - Chausse F.
PY - 2015
SP - 517
EP - 522
DO - 10.5220/0005304005170522