CUED Publications database

Systematic misregistration and the statistical analysis of surface data.

Gee, AH and Treece, GM (2013) Systematic misregistration and the statistical analysis of surface data. Med Image Anal, 18. pp. 385-393.

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Abstract

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.

Item Type: Article
Uncontrolled Keywords: Misregistration Spatial normalisation Statistical parametric mapping Algorithms Artifacts Diagnostic Errors Femur Head Humans Image Enhancement Models, Statistical Reproducibility of Results Sensitivity and Specificity
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:08
Last Modified: 23 Nov 2017 04:04
DOI: