Robust click-point linking for longitudinal follow-up studies

Kazunori Okada, Xiaolei Huang, Xiang Zhou, Arun Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging and Augmented Reality - Third International Workshop
PublisherSpringer Verlag
Pages252-260
Number of pages9
ISBN (Print)3540372202, 9783540372202
DOIs
StatePublished - 2006
Event3rd International Workshop on Medical Imaging and Augmented Reality - Shanghai, China
Duration: Aug 17 2006Aug 18 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4091 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Medical Imaging and Augmented Reality
CountryChina
CityShanghai
Period8/17/068/18/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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