Image intensity normalisation by maximising the Siddon line integral in the joint intensity distribution space
Date
2009-12ICR Author
Author
Kalemis, A
Binnie, DM
Flower, MA
Ott, RJ
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Research team
Radioisotope Physics
Language
eng
License start date
2009-12
Citation
MEDICAL IMAGE ANALYSIS, 2009, 13 pp. 900 - 909
Publisher
ELSEVIER SCIENCE BV