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2015•Unpublished venueRequires access

DTI Data Analysis

Victor Patrangenaru, Leif Ellingson

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Abstract

In Chapter 8, we presented general theory for nonparametric statistical ana- lysis of data from homogeneous Riemannian manifolds . Amongst other ap- plications of this methodology are diffusion tensor imaging (DTI) and cosmic microwave background radiation (CBR) (Schwartzman et al. (2008) [303]). Both of these applications led to the analysis of random objects on the set of positive definite symmetric matrices Sym+(m), for some dimension m ≥ 2. In this chapter, we will concentrate on application to DTI data analysis.

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What this paper is about

In Chapter 8, we presented general theory for nonparametric statistical ana- lysis of data from homogeneous Riemannian manifolds . Amongst other ap- plications of this methodology are diffusion tensor imaging (DTI) and cosmic microwave background radiation (CBR) (Schwartzman et al. (2008) [303]). Both of these applications led to the analysis of random objects on the set of positive definite symmetric matrices Sym+(m), for some dimension m ≥ 2. In this chapter, we will concentrate on application to DTI data analysis.

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Available abstract

In Chapter 8, we presented general theory for nonparametric statistical ana- lysis of data from homogeneous Riemannian manifolds . Amongst other ap- plications of this methodology are diffusion tensor imaging (DTI) and cosmic microwave background radiation (CBR) (Schwartzman et al. (2008) [303]). Both of these applications led to the analysis of random objects on the set of positive definite symmetric matrices Sym+(m), for some dimension m ≥ 2. In this chapter, we will concentrate on application to DTI data analysis.

Key concepts: Computer science

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