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

On the detectability by novices, radiologists, and computer algorithms of smallest increases in local single dot size in random-dot images