A New Brain Segmentation Framework

Abstract

We present a new brain segmentation framework which we apply to T1-weighted magnetic resonance image segmentation. The innovation of the algorithm in comparison to the state-of-the-art of non-supervised brain segmentation is twofold. First, the algorithm is entirely non-parametric and non-supervised. We can therefore enhance the classically used gray level information of the images by other features which do not fulfill the parametric Gaussian assumption. This is illustrated by a segmentation algorithm that considers both, voxel intensities and voxel gradients for the segmentation task. The resulting algorithm is called a non-supervised, non-parametric hidden Markov random field segmentation. Furthermore we have also to construct an anatomically relevant segmentation model in the resulting two-dimensional feature space. This is the second main contribution of this paper. We construct a morphologically inspired classification model, which is also able to segment the deep structures of the brain into a separate class, resulting in a six class segmentation model. We prove the validity of the introduced mathematical and morphological aspects on simulated T1-weighted magnetic resonance images of the brain.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.