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IPMI
2007
Springer

A Fuzzy, Nonparametric Segmentation Framework for DTI and MRI Analysis

10 years 10 months ago
A Fuzzy, Nonparametric Segmentation Framework for DTI and MRI Analysis
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 fuzzyC-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 datadriven statistical models. It produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-randomfield 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, ...
Suyash P. Awate, James C. Gee
Added 16 Nov 2009
Updated 16 Nov 2009
Type Conference
Year 2007
Where IPMI
Authors Suyash P. Awate, James C. Gee
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