algorithms.segmentation.segmentation¶
Module: algorithms.segmentation.segmentation
¶
Inheritance diagram for nipy.algorithms.segmentation.segmentation
:

Class¶
Segmentation
¶
-
class
nipy.algorithms.segmentation.segmentation.
Segmentation
(data, mask=None, mu=None, sigma=None, ppm=None, prior=None, U=None, ngb_size=26, beta=0.1)¶ Bases:
object
Methods
free_energy
([ppm])Compute the free energy defined as: log_external_field
()Compute the logarithm of the external field, where the external field is defined as the likelihood times the first-order component of the prior. map
()Return the maximum a posterior label map normalized_external_field
()run
([niters, freeze])set_markov_prior
(beta[, U])ve_step
()vm_step
([freeze])-
__init__
(data, mask=None, mu=None, sigma=None, ppm=None, prior=None, U=None, ngb_size=26, beta=0.1)¶ 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
Input image array
mask : array-like or tuple of array
Input mask to restrict the segmentation
beta : float
Markov regularization parameter
mu : array-like
Initial class-specific means
sigma : array-like
Initial class-specific variances
-
free_energy
(ppm=None)¶ 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).
-
log_external_field
()¶ Compute the logarithm of the external field, where the external field is defined as the likelihood times the first-order component of the prior.
-
map
()¶ Return the maximum a posterior label map
-
normalized_external_field
()¶
-
run
(niters=10, freeze=())¶
-
set_markov_prior
(beta, U=None)¶
-
ve_step
()¶
-
vm_step
(freeze=())¶
-
Functions¶
-
nipy.algorithms.segmentation.segmentation.
binarize_ppm
(q)¶ Assume input ppm is masked (ndim==2)
-
nipy.algorithms.segmentation.segmentation.
map_from_ppm
(ppm, mask=None)¶
-
nipy.algorithms.segmentation.segmentation.
moment_matching
(dat, mu, sigma, glob_mu, glob_sigma)¶ Moment matching strategy for parameter initialization to feed a segmentation algorithm.
Parameters: data: array :
Image data.
mu : array
Template class-specific intensity means
sigma : array
Template class-specific intensity variances
glob_mu : float
Template global intensity mean
glob_sigma : float
Template global intensity variance
Returns: dat_mu: array :
Guess of class-specific intensity means
dat_sigma: array :
Guess of class-specific intensity variances