A fuzzy, nonparametric segmentation framework for DTI and MRI analysis

Authors:
Suyash P. Awate;James C. Gee
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. those based on fuzzy-C-means (FCM), incorporate Gaussian class models which are inherently biased towards ellipsoidal clusters. Fiber bundles in DT images, however, comprise tensors that can inherently lie on more-complex manifolds. Unlike FCM-based schemes, the proposed method relies on modeling the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. It produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-random-field framework. For DT images, the paper describes a consistent statistical technique for nonparametric modeling in Riemannian DT spaces that incorporates two very recent works. In this way, the proposed method provides uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. The paper shows results on synthetic and real, DT as well as MR images.