  This study exploits previously demonstrated properties such as sensitivity to
the spatial extent and the intensity of local image contrast 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 show that the SOM QE consistently and in only a
few seconds detects fine regular spatial increases in relative amounts of RED
or GREEN pixel staining across the test images, reflecting small, systematic
increases or decreases in the percentage of theoretical cell viability below
the critical threshold. Such small changes may carry clinical significance, but
are almost impossible to detect by human vision. Moreover, we demonstrate a
clear sensitivity of the SOM QE to differences in the relative physical
luminance (Y) of the colors, which here translates into a RED GREEN color
selectivity. Across differences in relative luminance, the SOM QE exhibits
consistently greater sensitivity to the smallest spatial increases in RED image
pixels compared with smallest increases of identical spatial extents 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 biological data analysis using the SOM QE.
