  Time-series of images may reveal important information about changes in
medical or environmental conditions, depending on context. Visual inspection of
images by humans (experts or laymen) may fail in detecting very small
differences between images, yet, small but visually undetectable differences
may carry important significance. Computer algorithms may help overcome this
problem, and the use of computer driven image analysis in medical practice or
for the tracking of small but critical changes in natural environments attracts
a lot of interest. In many contexts relevant to society, the preprocessing of
large sets of image series will soon no longer be the exclusive realm of a few
scientists. Here we show that a metric obtained from self-organizing map
analysis (SOM) of image contents in time series of images of one and the same
object or environment reliably signals potentially critical local changes in
images that may not be detectable visually by a layman or even an expert.
