Module: metrics
¶
Compute Adapted Rand error as defined by the SNEMI3D contest. 

Return symmetric conditional entropies associated with the VI. 


Return the contingency table for all regions in matched segmentations. 

Compute the meansquared error between two images. 
Compute the normalized root meansquared error (NRMSE) between two images. 

Compute the peak signal to noise ratio (PSNR) for an image. 

Compute the mean structural similarity index between two images. 
adapted_rand_error¶

skimage.metrics.
adapted_rand_error
(image_true=None, image_test=None, *, table=None, ignore_labels=(0, ))[source]¶ Compute Adapted Rand error as defined by the SNEMI3D contest. [1]
 Parameters
 image_truendarray of int
Groundtruth 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.
 Returns
 arefloat
The adapted Rand error; equal to $1  frac{2pr}{p + r}$, where $p$ and $r$ are the precision and recall described below.
 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.
References
 1
ArgandaCarreras 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
Examples using skimage.metrics.adapted_rand_error
¶
variation_of_information¶

skimage.metrics.
variation_of_information
(image0=None, image1=None, *, table=None, ignore_labels=())[source]¶ Return symmetric conditional entropies associated with the VI. [1]
The variation of information is defined as VI(X,Y) = H(XY) + H(YX). If X is the groundtruth segmentation, then H(XY) can be interpreted as the amount of undersegmentation and H(XY) as the amount of oversegmentation. In other words, a perfect oversegmentation will have H(XY)=0 and a perfect undersegmentation will have H(YX)=0.
 Parameters
 image0, image1ndarray of int
Label images / segmentations, must have same shape.
 tablescipy.sparse array in csr format, optional
A contingency table built with skimage.evaluate.contingency_table. If None, it will be computed with skimage.evaluate.contingency_table. If given, the entropies will be computed from this table and any images will be ignored.
 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.
 Returns
 vindarray of float, shape (2,)
The conditional entropies of image1image0 and image0image1.
References
 1
Marina Meilă (2007), Comparing clusterings—an information based distance, Journal of Multivariate Analysis, Volume 98, Issue 5, Pages 873895, ISSN 0047259X, DOI:10.1016/j.jmva.2006.11.013.
Examples using skimage.metrics.variation_of_information
¶
contingency_table¶

skimage.metrics.
contingency_table
(im_true, im_test, *, ignore_labels=(), normalize=False)[source]¶ Return the contingency table for all regions in matched segmentations.
 Parameters
 im_truendarray of int
Groundtruth label image, same shape as im_test.
 im_testndarray of int
Test image.
 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.
 normalizebool
Determines if the contingency table is normalized by pixel count.
 Returns
 contscipy.sparse.csr_matrix
A contingency table. cont[i, j] will equal the number of voxels labeled i in im_true and j in im_test.
mean_squared_error¶

skimage.metrics.
mean_squared_error
(image0, image1)[source]¶ Compute the meansquared error between two images.
 Parameters
 image0, image1ndarray
Images. Any dimensionality, must have same shape.
 Returns
 msefloat
The meansquared error (MSE) metric.
Notes
Changed in version 0.16: This function was renamed from
skimage.measure.compare_mse
toskimage.metrics.mean_squared_error
.
normalized_root_mse¶

skimage.metrics.
normalized_root_mse
(image_true, image_test, *, normalization='euclidean')[source]¶ Compute the normalized root meansquared error (NRMSE) between two images.
 Parameters
 image_truendarray
Groundtruth image, same shape as im_test.
 image_testndarray
Test image.
 normalization{‘euclidean’, ‘minmax’, ‘mean’}, optional
Controls the normalization method to use in the denominator of the NRMSE. There is no standard method of normalization across the literature [1]. The methods available here are as follows:
‘euclidean’ : normalize by the averaged Euclidean norm of
im_true
:NRMSE = RMSE * sqrt(N) /  im_true 
where  .  denotes the Frobenius norm and
N = im_true.size
. This result is equivalent to:NRMSE =  im_true  im_test  /  im_true .
‘minmax’ : normalize by the intensity range of
im_true
.‘mean’ : normalize by the mean of
im_true
 Returns
 nrmsefloat
The NRMSE metric.
Notes
Changed in version 0.16: This function was renamed from
skimage.measure.compare_nrmse
toskimage.metrics.normalized_root_mse
.References
peak_signal_noise_ratio¶

skimage.metrics.
peak_signal_noise_ratio
(image_true, image_test, *, data_range=None)[source]¶ Compute the peak signal to noise ratio (PSNR) for an image.
 Parameters
 image_truendarray
Groundtruth image, same shape as im_test.
 image_testndarray
Test image.
 data_rangeint, optional
The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image datatype.
 Returns
 psnrfloat
The PSNR metric.
Notes
Changed in version 0.16: This function was renamed from
skimage.measure.compare_psnr
toskimage.metrics.peak_singal_noise_ratio
.References
structural_similarity¶

skimage.metrics.
structural_similarity
(im1, im2, *, win_size=None, gradient=False, data_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs)[source]¶ Compute the mean structural similarity index between two images.
 Parameters
 im1, im2ndarray
Images. Any dimensionality with same shape.
 win_sizeint or None, optional
The sidelength of the sliding window used in comparison. Must be an odd value. If gaussian_weights is True, this is ignored and the window size will depend on sigma.
 gradientbool, optional
If True, also return the gradient with respect to im2.
 data_rangefloat, optional
The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image datatype.
 multichannelbool, optional
If True, treat the last dimension of the array as channels. Similarity calculations are done independently for each channel then averaged.
 gaussian_weightsbool, optional
If True, each patch has its mean and variance spatially weighted by a normalized Gaussian kernel of width sigma=1.5.
 fullbool, optional
If True, also return the full structural similarity image.
 Returns
 mssimfloat
The mean structural similarity index over the image.
 gradndarray
The gradient of the structural similarity between im1 and im2 [2]. This is only returned if gradient is set to True.
 Sndarray
The full SSIM image. This is only returned if full is set to True.
 Other Parameters
 use_sample_covariancebool
If True, normalize covariances by N1 rather than, N where N is the number of pixels within the sliding window.
 K1float
Algorithm parameter, K1 (small constant, see [1]).
 K2float
Algorithm parameter, K2 (small constant, see [1]).
 sigmafloat
Standard deviation for the Gaussian when gaussian_weights is True.
Notes
To match the implementation of Wang et. al. [1], set gaussian_weights to True, sigma to 1.5, and use_sample_covariance to False.
Changed in version 0.16: This function was renamed from
skimage.measure.compare_ssim
toskimage.metrics.structural_similarity
.References
 1(1,2,3)
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600612. https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf, DOI:10.1109/TIP.2003.819861
 2
Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613621. arXiv:0901.0065 DOI:10.1007/s100430090119z