I'm using skimage.measure.marching_cubes
to extract a surface, defined as faces
and vertices
. marching_cubes
also outputs values
for each face.
How do I "smooth" these values
(the actual smoothing could be a low-pass filter, median filter etc)? I thought that one way to achieve this would be to project, or to represent this surface in 2D, and then apply standard filters, but I can't think of how to do this from a list of faces and vertices.
The reason for this "smoothing" is because the values are not informative at the scale of a single face of the surface, but over larger areas of the surface represented by many faces.
Thanks in advance!
I eventually found a way to do this, based on MATLAB code from this paper:
Welf et al. "Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments" in Developmental Cell, 2016, Vol 36, Issue 4, p462-475
def median_filter_surface(faces, verts, measure, radius, p_norm=2):
from scipy import spatial
import numpy as np
# INPUT:
# faces: triangular surface faces - defined by 3 vertices
# verts: the above vertices, defined by x,y,z coordinates
# measure: the value related to each face that needs to be filtered
# radius: the radius for median filtering (larger = more filtering)
# p_norm: distance metric for the radius, default 2 (euclidian)
# OUTPUT:
# measure_med_filt: the "measure" after filtering
num_faces = len(faces)
face_centres = np.zeros((num_faces, 3))
# get face centre positions in 3D space (from vert coordinates)
for face in range(0, num_faces):
face_centres[face, :] = np.mean(verts[faces[face, :], :], 0)
# return all other points within a radius
tree = spatial.KDTree(face_centres)
faces_in_radius = tree.query_ball_point(face_centres, radius, p_norm)
measure_med_filt = np.zeros(len(faces))
for face in range(0, len(faces)):
measure_med_filt[face] = np.median(measure[faces_in_radius[face]])
return measure_med_filt
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