Automatic subcortical structure segmentation using probabilistic atlas

Authors:
Jundong Liu;David Chelberg;Charles Smith;Hima Chebrolu
Affiliations:
School of Elec. Eng. & Comp. Sci., Ohio University, Athens, OH;School of Elec. Eng. & Comp. Sci., Ohio University, Athens, OH;Department of Neurology, University of Kentucky, Lexington, KY;Department of Neurology, University of Kentucky, Lexington, KY
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Year:
2007

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Abstract

Automatic segmentation of sub-cortical structures has great use in studying various neurodegentative diseases. In this paper, we propose a fully automatic solution to this problem through the utilization of a distribution atlas built from a set of training MR images. Our model consists of two major components: a local likelihood based active contour (LLAC) model and a guiding probabilistic atlas. The former has a very strong ability in standing out the structures that are in low contrast with the surrounding tissues. The latter has the functionality of defining and leading the segmentation procedure to capture the structure of interest. Formulated under the maximum a posterior framework, probabilistic atlas for the structure of interest, e.g. caudate, putamen, can be seamlessly integrated into the level set evolution procedure, and no thresholding step is needed for capturing the target.