AFQ.recognition.cleaning#
Module Contents#
Functions#
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Compute the cardinal orientation of each streamline |
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Clean a segmented fiber group based on the Mahalnobis distance of |
Attributes#
- AFQ.recognition.cleaning.clean_by_orientation(streamlines, primary_axis, tol=None)[source]#
Compute the cardinal orientation of each streamline
- Parameters
- streamlinessequence of N by 3 arrays
Where N is number of nodes in the array, the collection of streamlines to filter down to.
- Returns
- cleaned_idx, indicies of streamlines that passed cleaning
- AFQ.recognition.cleaning.clean_bundle(tg, n_points=100, clean_rounds=5, distance_threshold=3, length_threshold=4, min_sl=20, stat='mean', return_idx=False)[source]#
Clean a segmented fiber group based on the Mahalnobis distance of each streamline
- Parameters
- tgStatefulTractogram class instance or ArraySequence
A whole-brain tractogram to be segmented.
- n_pointsint, optional
Number of points to resample streamlines to. Default: 100
- clean_roundsint, optional.
Number of rounds of cleaning based on the Mahalanobis distance from the mean of extracted bundles. Default: 5
- distance_thresholdfloat, optional.
Threshold of cleaning based on the Mahalanobis distance (the units are standard deviations). Default: 3.
- length_threshold: float, optional
Threshold for cleaning based on length (in standard deviations). Length of any streamline should not be more than this number of stdevs from the mean length.
- min_slint, optional.
Number of streamlines in a bundle under which we will not bother with cleaning outliers. Default: 20.
- statcallable or str, optional.
The statistic of each node relative to which the Mahalanobis is calculated. Default: np.mean (but can also use median, etc.)
- return_idxbool
Whether to return indices in the original streamlines. Default: False.
- Returns
- ——-
- A StatefulTractogram class instance containing only the streamlines
- that have a Mahalanobis distance smaller than `clean_threshold` from
- the mean of each one of the nodes.