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Amira

-              v15
+              v16
 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:
+As a different project, one might want to track filaments in a 3d data cube and compare to the number of filaments found in projection. Here the projections might be evaluated using disperse. Take a look at this image of a neuron:
 [[Image(amira.png, 68%)]]
 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.
+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 more amenable to this segmentation approach. Also, one could use a physically meaningful density threshold to segment the data (i.e. the density at which molecular hydrogen can form efficiently). However, for now this project is going to be put on hold.