Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge.

Segmentation of the brain internal structures is an important and a challenging task due to their complex shapes, partial volume effects, low contrasts and anatomical variability between subjects. In this paper we propose a new non-rigid registration method that automatically segments the deep brain internal structures from brain MRI images. An atlas of the structures is used as a priori knowledge, which is modeled as a shape representation.

Hausdorff distance based 3D quantification of brain tumor evolution from MRI images.

This paper presents a quantification method which can be used to quantify the evolution of a brain tumor with time. From two segmented volumes, a Local Distance Volume (LDV) based on Hausdorff Distance is computed to show the true physical local distances between them. In the case of tracking a tumor volume during a therapeutic treatment, local variations can thus be shown by the LDV in particular where the tumor has regressed and where it has grown.