ants.segmentation.fuzzy_spatial_cmeans_segmentation

fuzzy_spatial_cmeans_segmentation(image, mask=None, number_of_clusters=4, m=2, p=1, q=1, radius=2, max_number_of_iterations=20, convergence_threshold=0.02, verbose=False)[source]

Fuzzy spatial c-means for image segmentation.

Image segmentation using fuzzy spatial c-means as described in

Chuang et al., Fuzzy c-means clustering with spatial information for image segmentation. CMIG: 30:9-15, 2006.

Parameters:

  • image (ants.core.ANTsImage) – Input image.

  • mask (ants.core.ANTsImage) – Optional mask image.

  • number_of_clusters (int) – Number of segmentation clusters.

  • m (float) – Fuzziness parameter (default=2).

  • p (float) – Membership importance parameter (default=1).

  • q (float) – Spatial constraint importance parameter (default=1). q = 0 is equivalent to conventional fuzzy c-means.

  • radius (int or tuple) – Neighborhood radius (scalar or array) for spatial constraint.

  • max_number_of_iterations (int) – Iteration limit (default=20).

  • convergence_threshold (float) – Convergence between iterations is measured using the Dice coefficient (default=0.02).

  • varbose (bool) – Print progress.

Return type:

dictionary containing ANTsImage and probability images

Example

>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> mask = ants.get_mask(image)
>>> fuzzy = ants.fuzzy_spatial_cmeans_segmentation(image, mask, number_of_clusters=3)