Abstract:
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.
