CASE-BASED DECISION STRATEGY USING OUTLIER PROBABILITY IN DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHIC LESIONS

In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or missed MCs in detection. To deal with difficulty, we propose a case-based decision strategy in MC detection, wherein we model the potential FPs in a detector output by a stochastic neighbor graph, and the MCs are characterized as statistical outliers. In the experiments, we demonstrated this approach on a set of 146 mammograms for two MC detectors. The results show that it could not only reduce the number of FPs (by as much as 39%), but more importantly, it could also reduce case-to-case variability in detection.