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Mattes mutual information metric configuration

A `MattesMutualInformation`

object describes a mutual
information metric configuration that you pass to the function `imregister`

to solve image registration problems.

You can create a `MattesMutualInformation`

object using the following
methods:

`imregconfig`

— Returns a`MattesMutualInformation`

object paired with an appropriate optimizer for registering multimodal imagesEntering

on the command line creates ametric = registration.metric.MattesMutualInformation;

`MattesMutualInformation`

object with default settings

Larger values of mutual information correspond to better registration results. You can examine the computed values of Mattes mutual information if you enable

`'DisplayOptimization'`

when you call`imregister`

, for example:movingRegistered = imregister(moving,fixed,'rigid',optimizer,metric,'DisplayOptimization',true);

Mutual information metrics are information theoretic techniques for measuring how related two variables are. These algorithms use the joint probability distribution of a sampling of pixels from two images to measure the certainty that the values of one set of pixels map to similar values in the other image. This information is a quantitative measure of how similar the images are. High mutual information implies a large reduction in the uncertainty (entropy) between the two distributions, signaling that the images are likely better aligned.

The Mattes mutual information algorithm uses a single set of pixel locations for the duration of the optimization, instead of drawing a new set at each iteration. The number of samples used to compute the probability density estimates and the number of bins used to compute the entropy are both user selectable. The marginal and joint probability density function is evaluated at the uniformly spaced bins using the samples. Entropy values are computed by summing over the bins. Zero-order and third-order B-spline kernels are used to compute the probability density functions of the fixed and moving images, respectively [1].

[1] Rahunathan, Smriti, D. Stredney, P. Schmalbrock, and B.D. Clymer. Image Registration Using Rigid Registration and Maximization of Mutual Information. Poster presented at: MMVR13. The 13th Annual Medicine Meets Virtual Reality Conference; 2005 January 26–29; Long Beach, CA.

[2] D. Mattes, D.R. Haynor, H. Vesselle, T. Lewellen, and W. Eubank. "Non-rigid
multimodality image registration." (Proceedings paper).*Medical Imaging 2001:
Image Processing*. SPIE Publications, 3 July 2001. pp.
1609–1620.

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