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

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.