Inheritance diagram for nipy.algorithms.segmentation.segmentation:
Bases: object
Methods
free_energy | |
log_external_field | |
map | |
normalized_external_field | |
run | |
set_markov_prior | |
ve_step | |
vm_step |
Class for multichannel Markov random field image segmentation using the variational EM algorithm. For details regarding the underlying algorithm, see:
Roche et al, 2011. On the convergence of EM-like algorithms for image segmentation using Markov random fields. Medical Image Analysis (DOI: 10.1016/j.media.2011.05.002).
Parameters : | data : array-like
mask : array-like or tuple of array
beta : float
mu : array-like
sigma : array-like
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Compute the free energy defined as:
F(q, theta) = int q(x) log q(x)/p(x,y/theta) dx
associated with input parameters mu, sigma and beta (up to an ignored constant).
Compute the logarithm of the external field, where the external field is defined as the likelihood times the first-order component of the prior.
Return the maximum a posterior label map
Assume input ppm is masked (ndim==2)
Moment matching strategy for parameter initialization to feed a segmentation algorithm.
Parameters : | data: array :
mu : array
sigma : array
glob_mu : float
glob_sigma : float
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Returns : | dat_mu: array :
dat_sigma: array :
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