Robust click-point linking for longitudinal follow-up studies

  • Authors:

  • Kazunori Okada;Xiaolei Huang;Xiang Zhou;Arun Krishnan

  • Affiliations:

  • Department of Computer Science, San Francisco State University;Computer-Aided Diagnosis and Therapy Solutions, Siemens Medical Solutions;Computer-Aided Diagnosis and Therapy Solutions, Siemens Medical Solutions;Computer-Aided Diagnosis and Therapy Solutions, Siemens Medical Solutions

  • Venue:
  • Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
  • Year:
  • 2006

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Abstract

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.