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Coupled Non-Parametric Shape and Moment-Based Inter-Shape Pose Priors for Multiple Basal Ganglia Structure Segmentation
Authors: Mustafa Gökhan Uzunbaş, Octavian Soldea, Devrim Ünay, Müjdat Çetin, Gözde Ünal, Aytül Erçil, Ahmet Ekin
Published in: IEEE Trans. Medical Imaging, vol. 29, no. 12, pp. 1959-1978, December 2010
Publication year: 2010
Abstract: This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principledmechanismto bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures inmagnetic resonance images.We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentationmethods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.
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