Three-dimensional image registration using distributed parallel computing

Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the rel...

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Bibliographic Details
Published in:IET image processing Vol. 12; no. 10; pp. 1713 - 1720
Main Authors: Song, Huajun, Qiu, Peihua
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.10.2018
Subjects:
3D image sequences
3D IR method
3D local kernel smoothing
distributed parallel computing
geometric transformation
image comparison
image monitoring
image registration
image sequences
medical imaging applications
parallel processing
Research Article
Taylor expansion
three‐dimensional image registration
3D image sequences
image registration
medical imaging applications
3D IR method
three-dimensional image registration
parallel processing
geometric transformation
image comparison
distributed parallel computing
3D local kernel smoothing
Taylor expansion
image sequences
image monitoring
ISSN:1751-9659, 1751-9667
Online Access:Get full text
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Summary:Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2017.1021