Computer-based detection and prompting of mammographic abnormalities

Abstract

Mammographic film reading is a highly demanding task, particularly in screening programmes where the reader must perform a detailed visual search of a large number of images for early signs of abnormality, which are often subtle or small, and which occur very infrequently. False negative cases, where signs of abnormality are missed by a film reader, are known to occur. Computer based algorithms can be used to detect abnormal patterns in images, but it is not possible to reliably detect all signs of abnormality in mammograms, so screening cannot yet be fully automated. The most successful detection algorithms are, however, incorporated in computer-aided detection (CAD) systems which indicate potentially abnormal locations to the reader in a process known as prompting. CAD systems have the capacity to reduce the frequency of false negative errors by ensuring that suspicious regions of the images are thoroughly searched and by increasing the weighting attached to subtle signs that may otherwise have been dismissed. One of the areas currently being researched is the effect of prompting on human performance. This is complex, since readers are presented with prompts generated by multiple detection algorithms, each of which has a different sensitivity and specificity. This paper reviews progress in abnormality detection, the strengths and the weaknesses of CAD, and the methodologies used to evaluate CAD in a clinical setting.

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