# Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection

## TL;DR

This study introduces a method using Self Organizing Maps to detect very small changes in medical and random-dot images, outperforming human novices in identifying subtle differences.

## Contribution

The paper presents a novel application of quantization errors from Self Organizing Maps for detecting minute image changes without relying on segmentation.

## Key findings

- Quantization errors increase with added lesions in MRI images.
- The method detects small local differences undetectable by humans.
- Results are consistent with previous alternative approaches.

## Abstract

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.

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Source: https://tomesphere.com/paper/1906.11675