Fully Automated Knowledge-Based Segmentation of the Caudate Nuclei in 3-D MRI
Michael Wels, Martin Huber, Joachim Hornegger · 2007
Abstract. In this paper we present a fully automated approach to segmentation of the caudate nuclei in 3-D magnetic resonance images. It is based on the technique of probabilistic boosting trees. As a strategy for supervised learning it is capable to derive a discriminative model for the distinction of object and non-object voxels from expert annotated imaging data and rather rough anatomical prior knowledge provided by a probabilistic anatomical atlas. Training the model involves successively selecting and combining features that best separate the available training samples, i.e., image voxels, and grouping the resulting boosted classifiers in a tree structure. Most of the features used are taken from an intra-axial 2-D context surrounding the voxel of interest and its transformation to a particular set of Haar-like features. The final segmentation is obtained after post-processing the preliminary result by a fast marching approach whose two speed images are seeded at the inner and outer bounds of the object detected. This allows for adaptation to local edges close to the initially detected object’s boundary. A detailed quantitative evaluation critically reveals strengths and weaknesses of the proposed method. 1