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b/ants/math/image_similarity.py |
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__all__ = ['image_similarity'] |
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import ants |
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from ants.decorators import image_method |
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@image_method |
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def image_similarity(fixed_image, moving_image, metric_type='MeanSquares', |
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fixed_mask=None, moving_mask=None, |
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sampling_strategy='regular', sampling_percentage=1.): |
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""" |
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Measure similarity between two images. |
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NOTE: Similarity is actually returned as distance (i.e. dissimilarity) |
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per ITK/ANTs convention. E.g. using Correlation metric, the similarity |
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of an image with itself returns -1. |
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ANTsR function: `imageSimilarity` |
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Arguments |
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--------- |
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fixed : ANTsImage |
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the fixed image |
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moving : ANTsImage |
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the moving image |
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metric_type : string |
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image metric to calculate |
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MeanSquares |
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Correlation |
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ANTSNeighborhoodCorrelation |
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MattesMutualInformation |
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JointHistogramMutualInformation |
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Demons |
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fixed_mask : ANTsImage (optional) |
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mask for the fixed image |
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moving_mask : ANTsImage (optional) |
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mask for the moving image |
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sampling_strategy : string (optional) |
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sampling strategy, default is full sampling |
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None (Full sampling) |
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random |
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regular |
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sampling_percentage : scalar |
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percentage of data to sample when calculating metric |
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Must be between 0 and 1 |
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Returns |
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------- |
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scalar |
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Example |
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------- |
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>>> import ants |
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>>> x = ants.image_read(ants.get_ants_data('r16')) |
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>>> y = ants.image_read(ants.get_ants_data('r30')) |
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>>> metric = ants.image_similarity(x,y,metric_type='MeanSquares') |
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""" |
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metric = ants.create_ants_metric(fixed_image, moving_image, metric_type, fixed_mask, |
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moving_mask, sampling_strategy, sampling_percentage) |
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return metric.get_value() |
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