skimage 0.17.2

ParametersReturns
compute_hessian_eigenvalues(image, sigma, sorting='none', mode='constant', cval=0)

For 2D images, the computation uses a more efficient, skimage-based algorithm.

Parameters

image : (N, ..., M) ndarray

Array with input image data.

sigma : float

Smoothing factor of image for detection of structures at different (sigma) scales.

sorting : {'val', 'abs', 'none'}, optional

Sorting of eigenvalues by values ('val') or absolute values ('abs'), or without sorting ('none'). Default is 'none'.

mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional

How to handle values outside the image borders.

cval : float, optional

Used in conjunction with mode 'constant', the value outside the image boundaries.

Returns

eigenvalues : (D, N, ..., M) ndarray

Array with (sorted) eigenvalues of Hessian eigenvalues for each pixel of the input image.

Compute Hessian eigenvalues of nD images.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


File: /skimage/filters/ridges.py#109
type: <class 'function'>
Commit: