Automatic brain segmentation method based on supervoxels

Martin Tamajka, Wanda Benesova

Abstract:

In this work, we present a fully automatic brain segmentation method based on supervoxels (ABSOS). We propose novel features used for classification, that are based on distance and angle in different planes between supervoxel and brain center. These novel features are combined with other prominent features. The presented method is based on machine learning and incorporates also a skull stripping (cranium removing) in the preprocessing step. Neural network – multilayer perceptron (MLP) was trained for the classification process. In this paper we also present thorough analysis, which supports choice of rather small supervoxels, preferring homogeneity over compactness, and value of intensity threshold parameter used in preprocessing for skull stripping. In order to decrease computational complexity and increase segmentation performance we incorporate prior knowledge of typical background intensities acquired in analysis of subjects.