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Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric k-means binning

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

Original languageEnglish
Title of host publicationIEEE International Symposium on Biomedical Imaging
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781479923748
Publication statusPublished - 21 Jul 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: 16 Apr 201519 Apr 2015

Publication series

NameIEEE International Symposium on Biomedical Imaging
ISSN (Print)1945-7928


Conference12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States


Multi-parametric MR image registration combines different imaging sequences to enhance visualisation and analysis. However, alignment of the different acquisitions is challenging, due to contrast-dependent anatomical information and abundant artefacts. For two decades, voxel-based registration has been dominated by methods based on mutual information, calculated from the joint image histogram. In this paper, we propose a modified framework - based on an asymmetric cluster-to-image mutual information metric - that increases registration speed and robustness. A new parameter, the homogeneous dynamic intensity range, is used to determine to which image clustering is applied. The framework also includes a semi-automatic 3D region of interest, multi-resolution wavelet decomposition, and particle swarm optimization. Performance of the framework, and its individual components, were evaluated on two diverse datasets, comprising cardiac and neonatal brain datasets. The results demonstrated the method was more robust and accurate than mutual information alone.

    Research areas

  • histogram specification, k-means binning, Multi-parametric registration, ROI-tracking


12th IEEE International Symposium on Biomedical Imaging, ISBI 2015


Brooklyn, United States

Event: Conference

ID: 19293308