Abstract：
:Mutual information (MI) has been widely used as a similarity measure for rigid registration of multi-modal and uni-modal medical images. However, robust application of MI to deformable registration is challenging mainly because rich structural information, which are critical cues for successful deformable registration, are not incorporated into MI. We propose a self-similarity weighted graph-based implementation of α-mutual information (α-MI) for nonrigid image registration. We use a self-similarity measure that uses local structural information and is invariant to rotation and to local affine intensity distortions, and therefore the new Self Similarity α-MI (SeSaMI) metric inherits these properties and is robust against signal nonstationarity and intensity distortions. We have used SeSaMI as the similarity measure in a regularized cost function with B-spline deformation field to achieve nonrigid registration. Since the gradient of SeSaMI can be derived analytically, the cost function can be efficiently optimized using stochastic gradient descent methods. We show that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Rivaz H,Karimaghaloo Z,Collins DLdoi
10.1016/j.media.2013.12.003subject
Has Abstractpub_date
2014-02-01 00:00:00pages
343-58issue
2eissn
1361-8415issn
1361-8423pii
S1361-8415(13)00174-6journal_volume
18pub_type
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journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2005.05.003
更新日期：2005-10-01 00:00:00
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doi：10.1016/j.media.2017.02.007
更新日期：2017-05-01 00:00:00
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journal_title：Medical image analysis
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pub_type： 杂志文章,评审
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pub_type： 杂志文章,评审
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