Fast and Robust 3-D MRI Brain Structure Segmentation

We present a novel method for the automatic detection and segmentation of (sub-)cortical gray matter structures in 3-D magnetic resonance images of the human brain. Essentially, the method is a topdown segmentation approach based on the recently introduced concept of Marginal Space Learning (MSL). We show that MSL naturally decomposes the parameter space of anatomy shapes along decreasing levels trical abstraction into subspaces of increasing dimensionality iting parameter invariance. At each level of abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. Contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-D generalized Haar features and steerable features derived from the observed volume intensities. Our system allows us to detect and segment 8 (sub-)cortical ...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Michael Wels, Yefeng Zheng, Gustavo Carneiro, Martin Huber, Joachim Hornegger, Dorin Comaniciu
• Keywords: 3-d Brain Mr, 3-d Generalized Haar, 3-d Mr Brain, Medical Imaging, MICCAI 2009