Combining cues for mammographic abnormalities

suspected abnormalities. Previous attempts to automate the detection of microcalcifications have used sequences of progressively more sophisticated methods to refine a set of candidates e.g. [2], though clinically acceptable error rates have not yet been achieved. Screening for breast cancer involves searching for subtle abnormalities in a large number of complex images, a task for which the specificity of human interpreters is known to be poor. We are aiming to improve screening performance by providing radiologists with machine assistance in the detection of clinically significant features. The first stage of the detection process is the generation of a set of cues to indicate potential abnormalities. We generally select a cue method for a particular task because it responds to a known property of the target. However, cue generators also respond to non-targets which share that target property. By combining evidence from a range of cues associated with different target properties we can increase the specificity of detection in noisy or cluttered images. We have performed experiments which demonstrate this. Two cue generators were applied to a set of 20 digitised image patches. Onand off-target distributions were collected for each image and accumulated across the data set on a leave-one-out basis. Each cue image was then transformed into a log-likelihood image, enabling evidence from the different cue generators to be combined simply by image addition. Results of an evaluation of single and combined cue methods are presented.