Open Access
June 2019 A Bayesian Approach to Statistical Shape Analysis via the Projected Normal Distribution
Luis Gutiérrez, Eduardo Gutiérrez-Peña, Ramsés H. Mena
Bayesian Anal. 14(2): 427-447 (June 2019). DOI: 10.1214/18-BA1113


This work presents a Bayesian predictive approach to statistical shape analysis. A modeling strategy that starts with a Gaussian distribution on the configuration space, and then removes the effects of location, rotation and scale, is studied. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows us to generalize the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.


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Luis Gutiérrez. Eduardo Gutiérrez-Peña. Ramsés H. Mena. "A Bayesian Approach to Statistical Shape Analysis via the Projected Normal Distribution." Bayesian Anal. 14 (2) 427 - 447, June 2019.


Published: June 2019
First available in Project Euclid: 23 June 2018

zbMATH: 07045437
MathSciNet: MR3934092
Digital Object Identifier: 10.1214/18-BA1113

Primary: 62F15 , 62H35
Secondary: 62J05

Keywords: Bookstein coordinates , Identifiability , medical image , shape regression

Vol.14 • No. 2 • June 2019
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