A Hierarchical Learning Approach for Detection of Clustered Microcalcifications in Mammograms
Juan Wang, Yongyi Yang · 2019
In computerized detection of clustered microcalcifications (MCs), the individual MCs in a mammogram image are typically identified by a pattern classifier based on the local image features at a location under consideration. In this work we investigate a hierarchical learning approach for detection of clustered MCs in which we exploit the property that the individual MCs in a cluster region tend to appear in close vicinity of each other. In the experiments we demonstrated the proposed approach on a set of 542 mammogram images and evaluated the detection performance by using free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach could effectively improve the detection accuracy by reducing the level of false positives (FPs).