Γ-convergence approximation to piecewise smooth medical image segmentation

Authors:
Jungha An;Mikael Rousson;Chenyang Xu
Affiliations:
Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, MN and Imaging and Visualization Department, Siemens Corporate Research, Princeton, NJ;Imaging and Visualization Department, Siemens Corporate Research, Princeton, NJ;Imaging and Visualization Department, Siemens Corporate Research, Princeton, NJ
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

Despite many research efforts, accurate extraction of structures of interest still remains a difficult issue in many medical imaging applications. This is particularly the case for magnetic resonance (MR) images where image quality depends highly on the acquisition protocol. In this paper, we propose a variational region based algorithm that is able to deal with spatial perturbations of the image intensity directly. Image segmentation is obtained by using a Γ-Convergence approximation for a multi-scale piecewise smooth model. This model overcomes the limitations of global region models while avoiding the high sensitivity of local approaches. The proposed model is implemented efficiently using recursive Gaussian convolutions. Numerical experiments on 2-dimensional human liver MR images show that our model compares favorably to existing methods.