Robust Click-Point Linking for Longitudinal Follow-Up Studies
This paper proposes a novel framework for robust click-point linking: efficient localized registration that allows users to interactively prescribe where the accuracy has to be high. Given a user-specified point in one domain, it estimates a single point-wise correspondence between a data domain pair. In order to link visually dissimilar local regions, we propose a new strategy that robustly establishes such a correspondence using only geometrical relations without comparing the local appearances. The solution is formulated as a maximum likelihood (ML) estimation of a spatial likelihood model without an explicit parameter estimation. The likelihood is modeled by a Gaussian mixture whose component describes geometric context of the click-point relative to pre-computed scale-invariant salient-region features. The local ML estimation was efficiently achieved by using variable-bandwidth mean shift. Two transformation classes of pure translation and scaling/translation are considered in this paper. The feasibility of the proposed approach is evaluated with 16 pairs of whole-body CT data, demonstrating the effectiveness.
KeywordsGaussian Mixture Model Salient Region Reference Domain Localize Registration Pure Translation
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