Dense Motion Estimation of the Heart Based on Cumulants
Abstract
Mutual Information (MI) has been extensively studied as similarity measure for the registration of medical images, and it has been found to be especially robust for multimodal image registration. However, MI estimators are known i) to have a very high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new similarity measure based on the sum of squared cumulants. In addition, our measure can be easily derivated with respect to registration parameters leading to an optimization with a simple gradient rule. Such a scheme is presented for a non-rigid registration and its performance is studied through computer results in the context of cardiac multislice computed tomography
Keywords
cardiology
computerised tomography
higher order statistics
image registration
medical image processing
motion estimation
optimisation
cardiac multislice computed tomography
dense motion estimation
medical image registration
multimodal image registration
mutual information
nonrigid registration
optimization
squared cumulants
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