Registration of 3D FMT and CT images of mouse via affine transformation with Bayesian iterative closest points

Xia Zheng, Xiaobo Zhou, Youxian Sun, Stephen T C Wong

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

3 Scopus citations

Abstract

It is difficult to directly co-register the 3D FMT (Fluorescence Molecular Tomography) image of a small tumor in a mouse whose maximal diameter is only a few mm with a larger CT image of the entire animal that spans about ten cm. This paper proposes a new method to register 2D flat and projected CT image first to facilitate the registration between small 3D FMT images and large CT images. And a novel algorithm Bayesian Iterative Closest Point (BICP) is introduced and validated in 2D affine registration. The visualization of the alignment of the 3D FMT and CT image through 2D registration shows promising results that would lead to automated 3D registration.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
PublisherSpringer-Verlag
Pages1140-1149
Number of pages10
EditionPART 2
ISBN (Print)9783540723929
DOIs
StatePublished - 2007
Event4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China
Duration: Jun 3 2007Jun 7 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Symposium on Neural Networks, ISNN 2007
CountryChina
CityNanjing
Period6/3/076/7/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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