Brain MRI Segmentation with Fast and Globally Convex Multiphase Active Contours

TL;DR

This paper presents a globally convex multiphase active contour model for brain MRI segmentation that guarantees optimal, stable, and accurate segmentation of multiple regions, outperforming existing methods especially under challenging conditions.

Contribution

It introduces a well-defined convex formulation and an efficient dual minimization scheme for brain MRI segmentation, ensuring optimal solutions and disjoint region segmentation.

Findings

Achieves better accuracy than existing multiphase active contour methods.

Effectively handles noise, intensity inhomogeneities, and partial volume effects.

Guarantees stable and optimal segmentation results.

Abstract

Multiphase active contour based models are useful in identifying multiple regions with different characteristics such as the mean values of regions. This is relevant in brain magnetic resonance images (MRIs), allowing the differentiation of white matter against gray matter. We consider a well defined globally convex formulation of Vese and Chan multiphase active contour model for segmenting brain MRI images. A well-established theory and an efficient dual minimization scheme are thoroughly described which guarantees optimal solutions and provides stable segmentations. Moreover, under the dual minimization implementation our model perfectly describes disjoint regions by avoiding local minima solutions. Experimental results indicate that the proposed approach provides better accuracy than other related multiphase active contour algorithms even under severe noise, intensity inhomogeneities, and partial volume effects.