bane/gkms tutorial: Step 3: Distilling the Histogram Volume

This tutorial does not work with any recent versions of Teem. Sorry.

As with the other gkms commands, you get usage info for distilling histogram volumes by supplying no arguments, and invoking just "gkms info":

gkms info: Project histogram volume for opacity function generation. This
distills the histogram volume down to the information required to create
either 1-D or 2-D opacity functions.

Usage: gkms info [-m <measr>] [-one] -i <hvolIn> -o <infoOut>

  -m <measr> = How to project along the 2nd derivative axis. Possibilities
               include:
             o "mean": average value
             o "median": value at 50th percentile
             o "mode": most common value
             o "min", "max": probably not useful
               default: "mean"
        -one = Create 1-dimensional info file; default is 2-dimensional
 -i <hvolIn> = input histogram volume (from "gkms hvol")
-o <infoOut> = output info file, used by "gkms pvg" and "gkms opac" (string)

There are not too many parameters here. The dimension of the info file is determined by the dimensionality of the opacity function you want to create. If you're feeling conservative and want to work only with traditional one-dimensional opacity functions:

gkms info -i engine-hvol.nrrd -one -o engine-info1.nrrd

Or, when you're ready to accept two-dimensional opacity functions into your life:

gkms info -i engine-hvol.nrrd -o engine-info2.nrrd

The "info" files generated by this step are arrays of floating point numbers which summarize important aspects of the histogram volume. Specifically, the 1-dimensional "info" files contain essentially g(v) and h(v), and the 2-dimensional "info" file contains essentially h(v,g). See this section of my MS thesis for definitions of these functions. One dimensional opacity functions are simple and familiar, but for many measured datasets the expressive power of two dimensional opacity functions facilitates a much higher level of control over which boundaries are rendered, and how.

There isn't any visual feedback for these programs, so there aren't any pictures to put on this page. Chances are, once you've generated the info files, you can delete the histogram volume, since it isn't used by any other gkms commands.

The "measr" parameter is not one which I've described in any of my published work, but which can be useful for analyzing histogram volumes from some more complicated datasets. ... More information as time permits ...