Segmentation of multimodal MRI of hippocampus using 3D grey-level morphology combined with artificial neural networks

Abstract

This paper presents an algorithm for improving the segmentation from a semi-automatic artificial neural network (ANN) hippocampus segmentation of co-registered T1-weigthted and T2-weighted MRI data, in which the semi-automatic part is the selection of a bounding-box. Due to the morphological complexity of the hippocampus and the difficulty of separating from adjacent structures, reproducible segmentation using MR imaging is complicated. The grey-level thresholding uses a histogram-based method to find robust thresholds. The T1-weighted data is grey-level eroded and dilated to reduce leaking from hippocampal tissue to the surrounding tissues and selecting possible foreground tissue. The method is a 3D approach, it uses 3 × 3 × 3 structure element for the grey-level morphology operations and the algorithms are applied in the three directions, sagittal, axial, and coronal, and the results are then combined together. © Springer-Verlag Berlin Heidelberg 2005.