A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction.

A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. Research Abstract Details 

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A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. Abstract Text:

Suyash P Awate,Hui Zhang,James C Gee,Suyash P Awate,Hui Zhang,James C Gee,

This paper presents a novel fuzzy-segmentation method for diffusion tensor (DT) and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g., those based on fuzzy C means (FCM), incorporate Gaussian class models that are inherently biased towards ellipsoidal clusters characterized by a mean element and a covariance matrix. Tensors in fiber bundles, however, inherently lie on specific manifolds in Riemannian spaces. Unlike FCM-based schemes, the proposed method represents these manifolds using nonparametric data-driven statistical models. The paper describes a statistically-sound (consistent) technique for nonparametric modeling in Riemannian DT spaces. The proposed method produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-random-field framework. Results on synthetic and real, DT and MR images, show that the proposed method provides information about the uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. By enhancing the nonparametric model to capture the spatial continuity and structure of the fiber bundle, we exploit the framework to extract the cingulum fiber bundle. Typical tractography methods for tract delineation, 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, such as the cingulum. The results demonstrate that the proposed method extracts this structure significantly more accurately as compared to tractography.

A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. Publishing Authors By Initials

SP Awate,H Zhang,JC Gee,SP Awate,H Zhang,JC Gee,

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A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: IEEE transactions on medical imaging

VOLUME: 26

Page Numbers: 1525-36

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ISSN: 0278-0062

DAY: 28

MONTH: Nov

YEAR: 2007

A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction. Information

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LANGUAGE: eng

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Grant and Affiliation Information for A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction.

AFFILIATION: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA. awate@mail.med.upenn.edu

Country: United States

AGENCY: United States NINDS

GRANT: NS045839

ACRONYM: NS

MEDLINETA: IEEE Trans Med Imaging

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