Intrasubject multimodal groupwise registration with the conditional template entropy

Mathias Polfliet*, Stefan Klein, Wyke Huizinga, Margarethus M. Paulides, Wiro J. Niessen, Jef Vandemeulebroucke

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    20 Citations (Scopus)
    24 Downloads (Pure)


    Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information.

    Original languageEnglish
    Pages (from-to)15-25
    Number of pages11
    JournalMedical Image Analysis
    Publication statusPublished - 2018


    • Conditional entropy
    • Groupwise image registration
    • Multimodal
    • Mutual information
    • Principal component analysis


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