  Radiologists use time series of medical images to monitor the progression of
a patient condition. They compare information gleaned from sequences of images
to gain insight on progression or remission of the lesions, thus evaluating the
progress of a patient condition or response to therapy. Visual methods of
determining differences between one series of images to another can be
subjective or fail to detect very small differences. We propose the use of
quantization errors obtained from Self Organizing Maps for image content
analysis. We tested this technique with MRI images to which we progressively
added synthetic lesions. We have used a global approach that considers changes
on the entire image as opposed to changes in segmented lesion regions only. We
claim that this approach does not suffer from the limitations imposed by
segmentation, which may compromise the results. Results show quantization
errors increased with the increase in lesions on the images. The results are
also consistent with previous studies using alternative approaches. We then
compared the detectability ability of our method to that of human novice
observers having to detect very small local differences in random-dot images.
The quantization errors of the SOM outputs compared with correct positive
rates, after subtraction of false positive rates (guess rates), increased
noticeably and consistently with small increases in local dot size that were
not detectable by humans. We conclude that our method detects very small
changes in complex images and suggest that it could be implemented to assist
human operators in image based decision making.
