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MICCAI
2008
Springer

Markov Dependence Tree-Based Segmentation of Deep Brain Structures

10 years 17 days ago
Markov Dependence Tree-Based Segmentation of Deep Brain Structures
We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In the images, the structure boundaries may be blurry or even missing, and the surrounding background is a clutter and full of irrelevant edges. We suggest a templatebased framework, which fuses the information of edge features, region statistics and inter-structure constraints to detect and locate all the targeted brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree. It makes the matching of multiple objects efficient. Our approach needs only one example as training data and alleviates the demand of a large training set. The obtained segmentation results on real data are encouraging and the proposed method enjoys several important advantages over existing methods.
Jue Wu, Albert C. S. Chung
Added 06 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where MICCAI
Authors Jue Wu, Albert C. S. Chung
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