Thalamus segmentation from diffusion tensor magnetic resonance imaging

Authors:
Ye Duan;Xiaoling Li;Yongjian Xi
Affiliations:
Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia, MO;Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia, MO;Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia, MO
Venue:
Journal of Biomedical Imaging
Year:
2007

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Abstract

We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from diffusion tensor magnetic resonance imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift-based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift-based algorithm supports hierarchical clustering naturally.