Abstract:
Radiologists use time-series of medical images to monitor the progression of a patient's conditions. 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's 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 (SOM) 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 imagebased
decision making.
