Bayesian principal geodesic analysis for estimating intrinsic diffeomorphic image variability.

Abstract:

:In this paper, we present a generative Bayesian approach for estimating the low-dimensional latent space of diffeomorphic shape variability in a population of images. We develop a latent variable model for principal geodesic analysis (PGA) that provides a probabilistic framework for factor analysis in the space of diffeomorphisms. A sparsity prior in the model results in automatic selection of the number of relevant dimensions by driving unnecessary principal geodesics to zero. To infer model parameters, including the image atlas, principal geodesic deformations, and the effective dimensionality, we introduce an expectation maximization (EM) algorithm. We evaluate our proposed model on 2D synthetic data and the 3D OASIS brain database of magnetic resonance images, and show that the automatically selected latent dimensions from our model are able to reconstruct unobserved testing images with lower error than both linear principal component analysis (LPCA) in the image space and tangent space principal component analysis (TPCA) in the diffeomorphism space.

journal_name

Med Image Anal

journal_title

Medical image analysis

authors

Zhang M,Fletcher PT

doi

10.1016/j.media.2015.04.009

subject

Has Abstract

pub_date

2015-10-01 00:00:00

pages

37-44

issue

1

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(15)00059-6

journal_volume

25

pub_type

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