Segmentation of Skull in 3D Human MR Images Using Mathematical Morphology

Abstract

We present a new technique for segmentation of skull in human T1-weighted magnetic resonance (MR) images that generates realistic models of the head for EEG and MEG source modeling. Our method performs skull segmentation using a sequence of mathematical morphological operations. Prior to the segmentation of skull, we segment the scalp and the brain from the MR image. The scalp mask allows us to quickly exclude background voxels with intensities similar to those of the skull, while the brain mask obtained from our Brain Surface Extractor algorithm ensures that the brain does not intersect our skull segmentation. We find the inner and the outer skull boundaries using thresholding and morphological closing and opening operations. We then mask the results with the scalp and brain volumes to ensure closed and nonintersecting skull boundaries. We applied our scalp and skull segmentation algorithm to several MR images and validated our method using coregistered CT-MR image data sets. We observe that our method is capable of producing scalp and skull segmentations suitable for MEG and EEG source modeling in 3D T1-weighted human MR images