fuzzy_cmeans¶
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nighres.segmentation.
fuzzy_cmeans
(image, clusters=3, max_iterations=50, max_difference=0.01, smoothing=0.1, fuzziness=2.0, mask_zero=True, save_data=False, overwrite=False, output_dir=None, file_name=None)[source]¶ Fuzzy C-means image segmentation
Estimates intensity clusters with spatial smoothness and partial voluming. Based on the RFCM algorithm of (Pham, 2001).
Parameters: - image (niimg) – Input image to segment
- clusters (int) – Number of clusters to estimate (default is 3)
- max_iterations (int) – Maximum number of iterations to perform (default is 50)
- max_difference (float) – Maximum difference between steps for stopping (default is 0.01)
- smoothing (float) – Ratio of spatial smoothness to impose on the clusters (default is 0.1)
- fuzziness (float) – Scaling of the C-means measure, in [1.0 - 3.0] (default is 2.0)
- mask_zero (bool) – Whether to ignore zero values (default is true)
- save_data (bool) – Save output data to file (default is False)
- overwrite (bool) – Overwrite existing results (default is False)
- output_dir (str, optional) – Path to desired output directory, will be created if it doesn’t exist
- file_name (str, optional) – Desired base name for output files with file extension (suffixes will be added)
Returns: Dictionary collecting outputs under the following keys (suffix of output files in brackets)
- memberships [niimg]: List of membership functions for each cluster in [0,1] (_rfcm-mem#cluster)
- classification (niimg): Hard classification of most likely cluster per voxel (_rfcm-class)
Return type: Notes
Original Java module by Pierre-Louis Bazin.