regionprops_3D#

regionprops_3D(im)[source]#

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

This functions offers a few extras for 3D images that are not provided by the regionprops function in scikit-image.

Parameters:

im (array_like) – An image 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