Using a geometric formulation of annular-like shape priors for constraining variational level-sets

Authors:
M. Alessandrini;T. Dietenbeck;O. Basset;D. Friboulet;O. Bernard
Affiliations:
ARCES, Universití di Bologna, Bologna, Italy;CREATIS, CNRS UMR5220, INSERM U630, Université de Lyon, Insa-Lyon, Villeurbanne, France;CREATIS, CNRS UMR5220, INSERM U630, Université de Lyon, Insa-Lyon, Villeurbanne, France;CREATIS, CNRS UMR5220, INSERM U630, Université de Lyon, Insa-Lyon, Villeurbanne, France;CREATIS, CNRS UMR5220, INSERM U630, Université de Lyon, Insa-Lyon, Villeurbanne, France
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

In this paper we address the segmentation of images exhibiting annular like shapes which may be approximated by two elliptical contours. Such patterns are indeed recurrent in many image processing applications. In this context, we develop a level-set framework specifically dedicated to the detection of annular shapes. Thanks to a fast solution to the least-squares fitting problem of similar patterns, our model handles the segmentation task efficiently with a single level-set function. The behavior of this approach is illustrated on images from various fields. An evaluation is then performed for the myocardium detection in MRI and ultrasound cardiac images.