imGeodesics

The imGeodesics modules provides functions for computing distance function in images, and computing geodesic parameters.

The base function is imChamferDistance. It propagates distances from a set of markers, using a mask to constrain the propagation. Chamfer distances are used as approximation of euclidean distances. The function imChamferDistance3d is its extension for 3D images.

Using Chamfer distance propagation, it is possible to compute geodesic distances and geodesic path between two markers in a particle. It is also possible to compute morphological parameters, such as the geodesic length or the geodesic radius. For some parameters, an exhaustive search is performed, making the computation time rather high.

Some functions are defined for 2D and 3D images. Some other exist in two different forms. The computation of geodesic path is not implemented for 3D images.

Function list

Distance propagation

imChamferDistance     - Compute chamfer distance using scanning algorithm
imChamferDistance3d   - Compute chamfer distance in 3D images
imGeodesicPath        - Compute a geodesic path between two markers in an image
imMaxGeodesicPath     - Find a path in a region with maximal geodesic length
imGeodesicDistance    - Compute geodesic distance between 2 markers

Geodesic parameters

imGeodesicDiameter    - Compute geodesic diameter of particles
imGeodesicDiameter3d  - Compute geodesic diameter of 3D particles
imGeodesicCenter      - Compute geodesic center of a binary particle
imGeodesicExtremities - Compute geodesic extremities of a binary particle
imGeodesicRadius      - Compute the geodesic radius of a binary particle
imGeodesicPropagation - Compute geodesic propagation for each foreground pixel

Validation

chamferDistanceError  - Compute relative error of chamfer distance with euclidean

References

If you find this package useful, please make a citation to the paper it was developed for:

• Legland, D. and Beaugrand, J. (2013). Automated clustering of lignocellulosic fibres based on morphometric features and using clustering of variables. Industrial Crops and Products, 45, 253-261. URL: http://dx.doi.org/10.1016/j.indcrop.2012.12.021

Another reference of interest:

• Lantuéjoul, C. and Beucher, S. (1981). On the use of geodesic metric in image analysis. J. Miscrosc., 121, 39-40

See also the imMeasures and the imMinkowski modules, for complementary image analysis functions.