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medpy.metric.binary.dc

# Metric measures (medpy.metric)¶

This package provides a number of metric measures that e.g. can be used for testing and/or evaluation purposes on two binary masks (i.e. measuring their similarity) or distance between histograms.

## Binary metrics (medpy.metric.binary)¶

Metrics to compare binary objects and classification results.

### Compare two binary objects¶

 dc(result, reference) Dice coefficient jc(result, reference) Jaccard coefficient hd(result, reference[, voxelspacing, …]) Hausdorff Distance. asd(result, reference[, voxelspacing, …]) Average surface distance metric. assd(result, reference[, voxelspacing, …]) Average symmetric surface distance. precision(result, reference) Precison. recall(result, reference) Recall. sensitivity(result, reference) Sensitivity. specificity(result, reference) Specificity. true_positive_rate(result, reference) True positive rate. true_negative_rate(result, reference) True negative rate. positive_predictive_value(result, reference) Positive predictive value. ravd(result, reference) Relative absolute volume difference.

### Compare two sets of binary objects¶

 obj_tpr(result, reference[, connectivity]) The true positive rate of distinct binary object detection. obj_fpr(result, reference[, connectivity]) The false positive rate of distinct binary object detection. obj_asd(result, reference[, voxelspacing, …]) Average surface distance between objects. obj_assd(result, reference[, voxelspacing, …]) Average symmetric surface distance.

### Compare to sequences of binary objects¶

 volume_correlation(results, references) Volume correlation. volume_change_correlation(results, references) Volume change correlation.

## Image metrics (medpy.metric.image)¶

Some more image metrics (e.g. sls and ssd) can be found in medpy.filter.image.

 mutual_information(i1, i2[, bins]) Computes the mutual information (MI) (a measure of entropy) between two images.

## Histogram metrics (medpy.metric.histogram)¶

 chebyshev(h1, h2) Chebyshev distance. chebyshev_neg(h1, h2) Chebyshev negative distance. chi_square(h1, h2) Chi-square distance. correlate(h1, h2) Correlation between two histograms. correlate_1(h1, h2) Correlation distance. cosine(h1, h2) Cosine simmilarity. cosine_1(h1, h2) Cosine simmilarity. cosine_2(h1, h2) Cosine simmilarity. cosine_alt(h1, h2) Alternative implementation of the cosine distance measure. euclidean(h1, h2) Equal to Minowski distance with $$p=2$$. fidelity_based(h1, h2) Fidelity based distance. histogram_intersection(h1, h2) Calculate the common part of two histograms. histogram_intersection_1(h1, h2) Turns the histogram intersection similarity into a distance measure for normalized, positive histograms. jensen_shannon(h1, h2) Jensen-Shannon divergence. kullback_leibler(h1, h2) Kullback-Leibler divergence. manhattan(h1, h2) Equal to Minowski distance with $$p=1$$. minowski(h1, h2[, p]) Minowski distance. noelle_1(h1, h2) Extension of fidelity_based proposed by [R39]. noelle_2(h1, h2) Extension of fidelity_based proposed by [R40]. noelle_3(h1, h2) Extension of fidelity_based proposed by [R41]. noelle_4(h1, h2) Extension of fidelity_based proposed by [R42]. noelle_5(h1, h2) Extension of fidelity_based proposed by [R43]. quadratic_forms(h1, h2) Quadrativ forms metric. relative_bin_deviation(h1, h2) Calculate the bin-wise deviation between two histograms. relative_deviation(h1, h2) Calculate the deviation between two histograms.