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2017 | Supplement | Buchkapitel

Neighborhood Matching for Curved Domains with Application to Denoising in Diffusion MRI

verfasst von : Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian Yap

Erschienen in: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

Verlag: Springer International Publishing

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Abstract

In this paper, we introduce a strategy for performing neighborhood matching on general non-Euclidean and non-flat domains. Essentially, this involves representing the domain as a graph and then extending the concept of convolution from regular grids to graphs. Acknowledging the fact that convolutions are features of local neighborhoods, neighborhood matching is carried out using the outcome of multiple convolutions at multiple scales. All these concepts are encapsulated in a sound mathematical framework, called graph framelet transforms (GFTs), which allows signals residing on non-flat domains to be decomposed according to multiple frequency subbands for rich characterization of signal patterns. We apply GFTs to the problem of denoising of diffusion MRI data, which can reside on domains defined in very different ways, such as on a shell, on multiple shells, or on a Cartesian grid. Our non-local formulation of the problem allows information of diffusion signal profiles of drastically different orientations to be borrowed for effective denoising.

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Metadaten
Titel
Neighborhood Matching for Curved Domains with Application to Denoising in Diffusion MRI
verfasst von
Geng Chen
Bin Dong
Yong Zhang
Dinggang Shen
Pew-Thian Yap
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-66182-7_72