报告主题：图像配准和统计形状分析的流形上的贝叶斯模型
报告人： 张妙妙 博士后（美国麻省理工学院（MIT））
报告时间：2016年5月23日（周一）15:00
报告地点：校本部G507
邀请人：彭亚新
主办部门：永利数学系
报告摘要：Computing a concise representation of the anatomical variability found in large sets of images is an important first step in many statistical shape analyses. In this talk, we present a generative Bayesian approach for automatic dimensionality reduction of shape variability represented through diffeomorphic mappings. To achieve this, we develop a latent variable model for principal geodesic analysis (PGA) that provides a probabilistic framework for factor analysis on diffeomorphisms. Our key contribution is a Bayesian inference procedure for model parameter estimation and simultaneous detection of the effective dimensionality of the latent space. We evaluate our proposed model for atlas and principal geodesic estimation on the OASIS brain database of magnetic resonance images. We show that the automatically selected latent dimensions from our model are able to reconstruct unseen brain images with lower error than equivalent linear principal components analysis (LPCA) models in the image space.
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