Inheritance diagram for nipy.algorithms.registration.similarity_measures:
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use a bivariate Gaussian as a distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use a nonlinear regression model with Gaussian errors as a distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use a nonlinear regression model with Laplace distributed errors as a distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use Parzen windowing in the discrete case to estimate the distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use the normalized joint histogram as a distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Use Parzen windowing to estimate the distribution model
Bases: nipy.algorithms.registration.similarity_measures.SimilarityMeasure
Assume a joint intensity distribution model is given by self.dist
Re-normalize correlation.
Convert a squared normalized correlation to a proper log-likelihood associated with a registration problem. The result is a function of both the input correlation and the number of points in the image overlap.
See: Roche, medical image registration through statistical inference, 2001.
Parameters: | rho2: float :
npts: int :
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Returns: | ll: float :
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Convert a joint distribution model q(i,j) into a pointwise loss:
L(i,j) = - log q(i,j)/(q(i)q(j))
where q(i) = sum_j q(i,j) and q(j) = sum_i q(i,j)
See: Roche, medical image registration through statistical inference, 2001.