Automatic brain segmentation method based on supervoxels

Automatic brain segmentation method based on supervoxels

Martin Tamajka, Wanda Benesova

 Faculty of Informatics and Information technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 842 16 Bratislava 4, Slovakia ;

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 centre. 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.

Published in:

2016 International Conference on Systems, Signals and Image Processing (IWSSIP)

Date of Conference:

23-25 May 2016

Download: Master’s Thesis – Bc. Martin Tamajka: Segmentation of anatomical organs in medical data