Defeaturing

Defeaturing is accomplished by specifying a voxel threshold. A cluster of voxels, defined as two or more voxels that share a face (edge and node sharing do not constitute a cluster) with count at or above the threshold will be preserved, whereas a cluster of voxels with a count below the threshold will be eliminated through resorption into the surrounding material.

Example

With Python, we created a segmentation of four circular blobs with noise placed randomly in a bounding box, shown in left of the figure below. The segmentation file blobs.npy was then used as the input to automesh with the defeature command. The output file blobs_defeatured.npy is shown in the right of the figure. The threshold was set to 20 voxels.

automesh defeature -i blobs.npy -o blobs_defeatured.npy -m 20

Both the segmentation files, original and defeatured, were then converted to a mesh and visualized in Hexalab.

Figure: (left) Four circular blobs with noise (blobs.npy) used as input to the defeature command, (right) the output defeatured segmentation (blobs_defeatured.npy).

Source

defeature.py