Image intensity normalisation by maximising the Siddon line integral in the joint intensity distribution space.
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.
Kalemis
A
A
Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT, UK. antonis.kalemis@philips.com
Binnie
D M
DM
Flower
M A
MA
Ott
R J
RJ
D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity