2007 | OriginalPaper | Buchkapitel
Fuzzy Nonparametric DTI Segmentation for Robust Cingulum-Tract Extraction
verfasst von : Suyash P. Awate, Hui Zhang, James C. Gee
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
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This paper presents a novel segmentation-based approach for fiber-tract extraction in diffusion-tensor (DT) images. Typical tractography methods, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. For these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, e.g. the
cingulum
. Unlike tractography—which disregards the information in the tensors that were previously tracked—the proposed method extracts the cingulum by exploiting the statistical coherence of tensors in the entire structure. Moreover, the proposed segmentation-based method allows
fuzzy
class memberships to optimally extract information within partial-volumed voxels. Unlike typical fuzzy-segmentation schemes employing Gaussian models that are biased towards ellipsoidal clusters, the proposed method
models the manifolds
underlying the classes by incorporating nonparametric data-driven statistical models. Furthermore, it exploits the nonparametric model to capture the
spatial continuity and structure
of the fiber bundle. The results on real DT images demonstrate that the proposed method extracts the cingulum bundle significantly more accurately as compared to tractography.