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dc.contributor.authorKalemis, Aen_US
dc.contributor.authorBinnie, DMen_US
dc.contributor.authorFlower, MAen_US
dc.contributor.authorOtt, RJen_US
dc.identifier.citationMEDICAL IMAGE ANALYSIS, 2009, 13 pp. 900 - 909en_US
dc.description.abstractThis 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.en_US
dc.format.extent900 - 909en_US
dc.titleImage intensity normalisation by maximising the Siddon line integral in the joint intensity distribution spaceen_US
dc.typeJournal Article
rioxxterms.typeJournal Article/Reviewen_US
pubs.notesaffiliation: Kalemis, A (Reprint Author), Philips Healthcare, Philips Ctr, Guildford Business Pk, Guildford GU2 8XH, Surrey, England. Kalemis, A.; Binnie, D. M.; Flower, M. A.; Ott, R. J., Inst Canc Res, Joint Dept Phys, Sutton SM2 5PT, Surrey, England. Kalemis, A.; Binnie, D. M.; Flower, M. A.; Ott, R. J., Royal Marsden NHS Fdn Trust, Sutton SM2 5PT, Surrey, England. Binnie, D. M., Univ London Imperial Coll Sci Technol & Med, Blackett Lab, London SW7 2AZ, England. keywords: Intensity; Normalization; Scaling keywords-plus: EMISSION TOMOGRAPHY; ROI DEFINITION; UPTAKE VALUES; PET; RESOLUTION; MODELS; TRIALS; SPECT research-areas: Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging web-of-science-categories: Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging author-email: [email protected] number-of-cited-references: 25 times-cited: 1 usage-count-last-180-days: 0 usage-count-since-2013: 2 journal-iso: Med. Image Anal. doc-delivery-number: 528LP unique-id: ISI:000272447500006 da: 2018-07-25en_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radioisotope Physics
pubs.embargo.termsNot knownen_US
icr.researchteamRadioisotope Physicsen_US
dc.contributor.icrauthorKalemis, Antonien_US

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