Abstract:
This study exploits previously demonstrated properties (i.e. sensitivity to spatial extent and intensity of local image contrasts) of the quantization error in the
output of a Self-Organizing Map (SOM-QE). Here, the SOM-QE is applied to
double-color-staining based cell viability data in 96 image simulations. The results
from this study show that, as expected, SOM-QE consistently and in only a few
seconds detects fine regular spatial increase in relative amounts of RED or
GREEN pixel staining across the test images, reflecting small, systematic increase
or decrease in the percentage of theoretical cell viability below a critical threshold.
While such small changes may carry clinical significance, they are almost impossible to detect by human vision. Moreover, here we demonstrate an expected sensitivity of the SOM-QE to differences in the relative physical luminance (Y) of the
colors, which translates into a RED-GREEN color selectivity. Across differences
in relative luminance, the SOM-QE exhibits consistently greater sensitivity to the
smallest spatial increase in RED image pixels compared with smallest increases of
the same spatial magnitude in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of
fast, economic, and unprecedentedly precise predictive imaging data analysis
based on SOM-QE.
