CellProfiler/CellProfiler — Issue #235: JIRA [IMG-183] MeasureRadialIntensity for objects with branching appearances
Author: thouis
Created: 2011-11-23 20:50:45+00:00
State: open

I'm putting this in Jira as a place to discuss the MeasureRadialIntensity measurements...
The MeasureRadialIntensity algorithm works by applying a metric to each pixel in an object:
distance_to_center
--------------------------
distance_to_center + distance_to_edge
This yields a number between 0 and 1. The algorithm then assigns pixels to bins by calculating n / # bins and assigning pixels with values less than n/bins but greater than n-1/bins to bin n.
This works well for circular objects because distance_to_center + distance_to_edge = radius. For oddly-shaped cells, especially neuron dendrites, the distance_to_center can be very much larger than distance_to_edge for most of the pixels in the object. Most of the pixels end up in the last bin.
Thinking of things in terms of maximum entropy, you'd get the most information from the measurement if you used the metric for ordering the pixels. You would create N bins per object, each of which would hold the same number of pixels. The measurements, FracAtD and MeanFrac, end up being equivalent if you do this.
Alternatively, you could use the Euclidean distance from the center or the propagation distance from the center as the binning metric; the current metric performs better if the magnification is anisotropic and performs"interestingly" if cells are ellipsoid (it calculates a direction-dependent radius).

Original reporter: leek
Jira link: https://jira.broadinstitute.org/browse/IMG-183
Assignee: leek

**Comment from anne on Fri, 16 Jul 2010 09:47:44 -0400:**
Would be nice to ponder sometime.