Improvements in Level Set Segmentation of 3D Small Animal Imagery

Abstract

In this paper, we investigate several improvements to region-based level set algorithms in the context of seg- menting x-ray CT data from pre-clinical imaging of small animal models. We incorporate a recently introduced signed distance preserving term into a region-based level set model and provide formulas for a semi-implicit finite difference implementation. We illustrate some pitfalls of topology preserving level sets and introduce the concept of connectivity preservation as a potential alternative. We illustrate the benefits of these improvements on phantom and real data.