skimage2.metrics.adapted_rand_error#
- skimage2.metrics.adapted_rand_error(image_true=None, image_test=None, *, table=None, ignore_labels=(0,), alpha=0.5)[source]#
Compute Adapted Rand error as defined by the SNEMI3D contest. [1]
- Parameters:
- image_truendarray of int
Ground-truth label image, same shape as im_test.
- image_testndarray of int
Test image.
- tablescipy.sparse array in crs format, optional
A contingency table built with skimage.evaluate.contingency_table. If None, it will be computed on the fly.
- ignore_labelssequence of int, optional
Labels to ignore. Any part of the true image labeled with any of these values will not be counted in the score.
- alphafloat, optional
Relative weight given to precision and recall in the adapted Rand error calculation.
- Returns:
- arefloat
The adapted Rand error.
- precfloat
The adapted Rand precision: this is the number of pairs of pixels that have the same label in the test label image and in the true image, divided by the number in the test image.
- recfloat
The adapted Rand recall: this is the number of pairs of pixels that have the same label in the test label image and in the true image, divided by the number in the true image.
Notes
Pixels with label 0 in the true segmentation are ignored in the score.
The adapted Rand error is calculated as follows:
\(1 - \frac{\sum_{ij} p_{ij}^{2}}{\alpha \sum_{k} s_{k}^{2} + (1-\alpha)\sum_{k} t_{k}^{2}}\), where \(p_{ij}\) is the probability that a pixel has the same label in the test image and in the true image, \(t_{k}\) is the probability that a pixel has label \(k\) in the true image, and \(s_{k}\) is the probability that a pixel has label \(k\) in the test image.
Default behavior is to weight precision and recall equally in the adapted Rand error calculation. When alpha = 0, adapted Rand error = recall. When alpha = 1, adapted Rand error = precision.
References
[1]Arganda-Carreras I, Turaga SC, Berger DR, et al. (2015) Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9:142. DOI:10.3389/fnana.2015.00142