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A patch-based framework for new ITK functionality: Joint fusion, denoising, and non-local super-resolution
|Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3570|
Submitted by Nick Tustison on 03-15-2017.
In an earlier Insight Journal article, we introduced an ITK implementation of the adaptive patch-based image denoising algorithm described in . We follow-up up that offering with a generalized non-local, patch-based ITK class framework and a refactored denoising class. In addition, we provide two ITK implementations of related, well-known algorithms. The first is a non-local super resolution method described in [1, 2]. The second is the multivariate joint label fusion algorithm of [4, 5] with additional extensions, denoted as “joint intensity fusion”, which will be described in a forthcoming manuscript. Accompanying these ITK classes are documented programming interfaces which use our previously introduced unique command line interface routines. Several 2-D examples on brain imaging data are provided to qualitatively demonstrate performance.
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|Categories:||Atlas-based segmentation, Filtering, Resampling, Segmentation|
|Keywords:||non-local, Patch based , image denoising, super-resolution, joint fusion|
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