October 6, 2015

Amira segments the data to do skeletonization. This means it throws out pixels below a certain user defined threshold. Of the remaining pixels it 1) finds a centerline through the data that, 2) is through the middle, and then 3) thins this line to be 1 pixel across. This is all to retain "homotopy" of the filamentary network. This is not ideal for column density data: as the threshold is lowered to account for lower density pixels, the structures get blown out and the skeletonization algorithm breaks down. Here are some images that gradually increase the threshold to illustrate this (smaller to larger thresholds):

From this, one can interactively click on the filament that is of interest, and get the resultant statistics for it. However, to sample a wide range of densities, this requires multiple thresholding steps, and probably performs worse than disperse.

As a different project, one might want to track filaments in a 3d data cube and compare to in projection. Here the projections might be evaluated using disperse or amira.. Take a look at this image of a neuron:

If the hypothesis that all filaments are r=0.1 pc across is true, then maybe one gets cleaner segments; that is, filamentary structure that is traceable via this segmentation approach.

October 5, 2015

The "auto" skeletonization method appears to take a threshold as an input. This threshold seems to 'mask' (or 'segment') the data so to only look at voxels with values above this threshold. It then does a 'centerline' analysis, a 'distance map' analysis, and finally a 'thinning' of the skeleton so that it is only 1 voxel across. To see the paper on this algorithm, it is attached to this page. It is also possible to do these 3 steps on your own in Amira, and thus gain more control over the skeletonization process.

Here are some results testing different thresholds in auto skeletonization mode, and comparing them to Federrath's figure (the paper that contains this figure is attached to this page).

There are a lot of clear filamentous structure that Amira is missing.