<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 ANTsPy and ANTsR inherit the ability to do multi-metric registration.
Such registrations assume that the feature images are in the same physical space and at least roughly aligned.
One may then use registration to optimize a multiple similarity metric objective function as in
Step1:  Perform a baseline registration with a single feature and create a couple of new metrics.  Each metric is defined by a name ("CC"), the input fixed (image), input moving (image2), a weight value (e.g. 2) and a sampling parameter ( for CC this defines a radius of 9x9 e.g. 4 extra pixels on all sides of the center pixel.  Five entries are needed in total.
Step2:  Append the first metric to the metric list. In actuality this means that reg2 will be driven by both a demons metric and the default metric.
Step3:  Add a third metric and run this new registration.
Step4:  Quantify the results in terms of mutual information of the registration results using the original image intensity.
<ASSISTANT_TASK:>
Python Code:
import ants
image = ants.image_read(ants.get_ants_data('r16'))
image2 = ants.image_read(ants.get_ants_data('r64'))
aff = ants.registration( image, image2, "Affine" )
g1 = ants.iMath_grad( image )
g2 = ants.iMath_grad( image2 )
Explanation: ANTsPy and ANTsR inherit the ability to do multi-metric registration.
Such registrations assume that the feature images are in the same physical space and at least roughly aligned.
One may then use registration to optimize a multiple similarity metric objective function as in:
https://doi.org/10.3389/fninf.2014.00044
https://www.ncbi.nlm.nih.gov/pubmed/18995188
First, import ants, read some images and create some features.
End of explanation
reg1 = ants.registration( image, image2, 'SyNOnly', initial_transform=aff['fwdtransforms'][0], verbose=False )
demonsMetric = ['demons', g1, g2, 1, 1]
ccMetric = ['CC', image, image2, 1.5, 4 ]
Explanation: Perform a baseline registration with a single feature and create a couple of new metrics.  Each metric is defined by a name ("CC"), the input fixed (image), input moving (image2), a weight value (e.g. 2) and a sampling parameter ( for CC this defines a radius of 9x9 e.g. 4 extra pixels on all sides of the center pixel.  Five entries are needed in total.
End of explanation
metrics = list( )
metrics.append( demonsMetric )
reg2 = ants.registration( image, image2, 'SyNOnly',
    multivariate_extras = metrics, initial_transform=aff['fwdtransforms'][0] )
Explanation: Append the first metric to the metric list. In actuality this means that reg2 will be driven by both a demons metric and the default metric.
End of explanation
metrics.append( ccMetric )
reg3 = ants.registration( image, image2, 'SyNOnly',
    multivariate_extras = metrics, initial_transform=aff['fwdtransforms'][0] )
Explanation: Add a third metric and run this new registration.
End of explanation
print( ants.image_mutual_information( image, image2 ) )
print( ants.image_mutual_information( image, reg1['warpedmovout'] ) )
print( ants.image_mutual_information( image, reg2['warpedmovout'] ) )
print( ants.image_mutual_information( image, reg3['warpedmovout'] ) )
ants.plot(reg1['warpedmovout'])
Explanation: Quantify the results in terms of mutual information of the registration results using the original image intensity.
End of explanation
<END_TASK>