# Metrics¶

Extract Quantitative Information

This submodule contains functions for determining key metrics about an image. Typically these are applied to an image after applying a filter, but a few functions can be applied directly to the binary image.

 Finds the length of each chord in the supplied image and returns a list of their individual sizes Determines the distribution of chord lengths in an image containing chords. `metrics.geometrical_tortuosity`(im[, axis]) Calculate geometrical tortuosity across an image `metrics.lineal_path_distribution`(im[, bins, …]) Determines the probability that a point lies within a certain distance of the opposite phase along a specified direction `metrics.mesh_surface_area`([mesh, verts, faces]) Calculates the surface area of a meshed region `metrics.phase_fraction`(im[, normed]) Calculates the number (or fraction) of each phase in an image `metrics.pore_size_distribution`(im[, bins, …]) Calculate a pore-size distribution based on the image produced by the `porosimetry` or `local_thickness` functions. Calculates the porosity of an image assuming 1’s are void space and 0’s are solid phase. `metrics.porosity_profile`(im[, axis]) Returns a porosity profile along the specified axis `metrics.prop_to_image`(regionprops, shape, prop) Creates an image with each region colored according the specified `prop`, as obtained by `regionprops_3d`. `metrics.props_to_DataFrame`(regionprops) Returns a Pandas DataFrame containing all the scalar metrics for each region, such as volume, sphericity, and so on, calculated by `regionprops_3D`. Computes radial density function by analyzing the histogram of voxel values in the distance transform. `metrics.region_interface_areas`(regions, areas) Calculates the interfacial area between all pairs of adjecent regions `metrics.region_surface_areas`(regions[, …]) Extracts the surface area of each region in a labeled image. Calculates various metrics for each labeled region in a 3D image. Calculates the porosity of the image as a function subdomain size. `metrics.two_point_correlation_bf`(im[, spacing]) Calculates the two-point correlation function using brute-force (see Notes) Calculates the two-point correlation function using fourier transforms.