Scale-Based Gaussian Coverings: Combining Intra and Inter Mixture Models in Image Segmentation

TL;DR

This paper introduces a novel approach to image segmentation using scale-based Gaussian coverings and Rényi quadratic entropy to compare models, demonstrated on MRI images, providing a clear method for model selection.

Contribution

It proposes using Rényi quadratic entropy as an effective framework for comparing Gaussian mixture models in image segmentation, integrating intra and inter mixture modeling.

Findings

Rényi quadratic entropy effectively distinguishes superior models.

Application to MRI segmentation demonstrates practical utility.

Gaussian mixture models can be compared quantitatively using this entropy measure.

Abstract

By a "covering" we mean a Gaussian mixture model fit to observed data. Approximations of the Bayes factor can be availed of to judge model fit to the data within a given Gaussian mixture model. Between families of Gaussian mixture models, we propose the R\'enyi quadratic entropy as an excellent and tractable model comparison framework. We exemplify this using the segmentation of an MRI image volume, based (1) on a direct Gaussian mixture model applied to the marginal distribution function, and (2) Gaussian model fit through k-means applied to the 4D multivalued image volume furnished by the wavelet transform. Visual preference for one model over another is not immediate. The R\'enyi quadratic entropy allows us to show clearly that one of these modelings is superior to the other.