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 Analjournal_title
Medical image analysisauthors
Zhang M,Fletcher PTdoi
10.1016/j.media.2015.04.009subject
Has Abstractpub_date
2015-10-01 00:00:00pages
37-44issue
1eissn
1361-8415issn
1361-8423pii
S1361-8415(15)00059-6journal_volume
25pub_type
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