regionprops_3D

regionprops_3D(im)[source]

Calculates various metrics for each labeled region in a 3D image.

The regionsprops method in skimage is very thorough for 2D images, but is a bit limited when it comes to 3D images, so this function aims to fill this gap.

Parameters

im (array_like) – An imaging containing at least one labeled region. If a boolean image is received than the True voxels are treated as a single region labeled 1. Regions labeled 0 are ignored in all cases.

Returns

props – An augmented version of the list returned by skimage’s regionprops. Information, such as volume, can be found for region A using the following syntax: result[A-1].volume.

The returned list contains all the metrics normally returned by skimage.measure.regionprops plus the following:

’slices’

Slice indices into the image that can be used to extract the region

’volume’

Volume of the region in number of voxels.

’bbox_volume’

Volume of the bounding box that contains the region.

’border’

The edges of the region, found as the locations where the distance transform is 1.

’inscribed_sphere’

An image containing the largest sphere can can fit entirely inside the region.

’surface_mesh_vertices’

Obtained by applying the marching cubes algorithm on the region, AFTER first blurring the voxel image. This allows marching cubes more freedom to fit the surface contours. See also surface_mesh_simplices

’surface_mesh_simplices’

This accompanies surface_mesh_vertices and together they can be used to define the region as a mesh.

’surface_area’

Calculated using the mesh obtained as described above, using the porespy.metrics.mesh_surface_area method.

’sphericity’

Defined as the ratio of the area of a sphere with the same volume as the region to the actual surface area of the region.

’skeleton’

The medial axis of the region obtained using the skeletonize_3D method from skimage.

’convex_volume’

Same as convex_area, but translated to a more meaningful name.

Return type

list

See also

snow_partitioning

Notes

Regions can be identified using a watershed algorithm, which can be a bit tricky to obtain desired results. PoreSpy includes the SNOW algorithm, which may be helpful.