Soft Image Segmentation Based on the Mixture of Gaussians and the Phase-Transition Theory

Celia A. Z. Barcelos¹, Yunmei Chen², Fuhua Chen³

¹Faculty of Mathematics, Federal University of Uberlandia, Uberlandia, MG, Brazil.
²Department of Mathematics, University of Florida, Gainesville, FL, USA.
³Department of Natural Science & Mathematics, West Liberty University, West Liberty, WV, USA.

DOI: 10.4236/am.2014.518275                

Abstract

In this paper, we propose a new soft multi-phase segmentation model where it is assumed that the pixel intensities are distributed as a Gaussian mixture. The model is formulated as a minimization problem through the use of the maximum likelihood estimator and phase-transition theory. The mixture coefficients, which are estimated using a spatially varying mean and variance procedure, are used for image segmentation. The experimental results indicate the effectiveness of the method.

Keywords

Image Segmentation, Variational Model, Gaussian Mixture

Conflicts of Interest

The authors declare no conflicts of interest.

References

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