Interpolation Artefacts in Mutual Information-Based Image Registration

Pluim, Josien P. W.; Maintz, J. B. Antoine; Viergever, Max A.

doi:10.1006/cviu.1999.0816

Cited by 232 publications

(182 citation statements)

References 12 publications

(12 reference statements)

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“…In most applications, it is of paramount importance to limit as much as possible the grey-value errors introduced by interpolation. For example, in multimodality registration of computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) data, it has been observed that interpolation errors influence the value of the optimization cost function, which may lead to registration errors [42]. Similar problems had been reported earlier in monomodality registration of MR images [13].…”

Section: Introductionsupporting

confidence: 52%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

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252

165

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Abstract-Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sincapproximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

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Section: Introductionsupporting

confidence: 52%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

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252

165

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“…The downsampling is avoided since it causes histogram changes, which in turn may cause some of the artefacts similar to the ones mentioned in [42].…”

Section: Optimization Algorithmmentioning

confidence: 99%

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Kalinić

Lončarić

Čikeš

et al. 2012

Computer Methods and Programs in Biomedicine

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“…This is less of a problem than it might seem, since optimisation functions sample the cost function surface quite sparsely and the local trends in surface are not strongly affected by hiss. STD and first order PVE are somewhat prone to bias due to glitches [2], which can be more serious. The effect of glitches is seen in some results, but further discussion is not possible here due to space constraints.…”

Section: Artefactsmentioning

confidence: 99%

“…The obvious exceptions are several papers that discuss artefacts on the MI cost function surface [2,3] and optimisation strategies [4]. However, only empirical analyses are made (unlike the analytic comparisons here).…”

Section: Introductionmentioning

confidence: 99%

A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

Dowson

Bowden

2006

Computer Vision – ECCV 2006

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Abstract. Many variants of MI exist in the literature. These vary primarily in how the joint histogram is populated. This paper places the four main variants of MI: Standard sampling, Partial Volume Estimation (PVE), In-Parzen Windowing and Post-Parzen Windowing into a single mathematical framework. Jacobians and Hessians are derived in each case. A particular contribution is that the non-linearities implicit to standard sampling and post-Parzen windowing are explicitly dealt with. These non-linearities are a barrier to their use in optimisation. Side-byside comparison of the MI variants is made using eight diverse data-sets, considering computational expense and convergence. In the experiments, PVE was generally the best performer, although standard sampling often performed nearly as well (if a higher sample rate was used). The widely used sum of squared differences metric performed as well as MI unless large occlusions and non-linear intensity relationships occurred. The binaries and scripts used for testing are available online.

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Interpolation Artefacts in Mutual Information-Based Image Registration

Cited by 232 publications

References 12 publications

Quantitative evaluation of convolution-based methods for medical image interpolation

Quantitative evaluation of convolution-based methods for medical image interpolation

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

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