Depth Potential Function
Depth Potential FunctionCompute the Depth Potential Function as described in:
Boucher, M, Whitesides, A, Evans, AC, Depth potential function for folding pattern representation, registration and analysis, Medical Image Analysis, 13(2):203-14, 2009.
The DPF is an estimation of the depth of the folds based on the concept of bending distance for curves. Technically, DPF is a scalar field corresponding to the signed traveled distance that quantifies how much a curve is bent inward or outward and can thus be interpreted in term of average convexity. Formally, it represents the overall shape of a fold as the function whose Laplacian is as close as possible to the mean curvature of the surface.
Therefore, the DPF can also be interpreted as a representation of the mean curvature at multiple scales with alpha being the parameter that controls how much emphasis to put on the finer scales. Thus, DPF relates to both average convexity and curvature through the trade-off parameter alpha, as illustrated on the figure. When alpha tends to infinity, the DPF tends to the mean curvature, when alpha tends to 0 the DPF tends to the average convexity, and for intermediate values of alpha, the DPF integrates both types of geometrical information.
input_mesh: Maillage de la matière blanche d'un hémisphère ( entrée )the DPF can be computed for any mesh with a spherical topology
DPF_texture: DPF texture ( sortie )
alphas: ListOf( Réel ) ( input )list of values for the parameter alpha. The output DPF texture will have the same number of times as the number of values given for this parameter.
Toolbox : Surface corticale
Niveau d'utilisateur : 0
Identifiant :
DepthPotentialFunctionNom de fichier :
brainvisa/toolboxes/cortical_surface/processes/anatomy/tools/DepthPotentialFunction.pySupported file formats :
input_mesh :GIFTI file, GIFTI file, Maillage MESH, MNI OBJ mesh, PLY mesh, Maillage TRIDPF_texture :GIFTI file, GIFTI file, Texture