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Image intensity normalisation by maximising the Siddon line integral in the joint intensity distribution space

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Date
2009-12
ICR Author
Kalemis, Antoni
Author
Kalemis, A
Binnie, DM
Flower, MA
Ott, RJ
Type
Journal Article
Metadata
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Abstract
This paper presents a novel data-driven method for image intensity normalisation, which is a prerequisite step for any kind of image comparison. The method involves a novel application of the Siddon algorithm that was developed initially for fast reconstruction of tomographic images and is based on a linear normalisation model with either one or two parameters. The latter are estimated by maximising the line integral, computed using the Siddon algorithm, in the 2D joint intensity distribution space of image pairs. The proposed normalisation method, referred to as Siddon Line Integral Maximisation (SLIM), was compared with three other methodologies, namely background ratio (BAR) scaling, linear fitting and proportional scaling, using a large number of synthesised datasets. SLIM was also compared with BAR normalisation when applied to phantom data and two clinical examples. The new method was found to be more accurate and less biased than its counterparts for the range of characteristics selected for the synthesised data. These findings were in agreement with the results from the analysis of the experimental and clinical data. (C) 2009 Elsevier B.V. All rights reserved.
URI
https://repository.icr.ac.uk/handle/internal/2152
DOI
https://doi.org/10.1016/j.media.2009.08.002
Collections
  • Radiotherapy and Imaging
Research team
Radioisotope Physics
Language
eng
License start date
2009-12
Citation
MEDICAL IMAGE ANALYSIS, 2009, 13 pp. 900 - 909
Publisher
ELSEVIER SCIENCE BV

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