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Intrasubject multimodal groupwise registration with the conditional template entropy.

Abstract：

: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.

journal_name

Med Image Anal

journal_title

Medical image analysis

authors

Polfliet M,Klein S,Huizinga W,Paulides MM,Niessen WJ,Vandemeulebroucke J

doi

10.1016/j.media.2018.02.003

subject

Has Abstract

pub_date

2018-05-01 00:00:00

pages

15-25

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(18)30028-8

journal_volume

46

pub_type

杂志文章

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