Surrounding Region Dependence Method for Detection of Clustered Microcalcifications on Mammograms

Clustered microcalcifications on X-ray mammograms are an important feature in the detection of breast cancer. For the detection of the clustered microcalcifications on digitized mammograms, this paper proposes a texture analysis method called the surrounding region dependence method (SRDM), which is a statistical texture analysis based on the second-order histogram in two surrounding region. Four textural features are extracted from the SRDM. These features are used to class@ region of interests (ROls) into positive ROIs containing clustered microcalcifications and negative ROIs of normal breast tissues. The three-layer back propagation neural network is employed as a classifier with input data of four textural features. The classification performance of the proposed method is evaluated by using the round-robin method and the receiver operating-characteristics (ROC) analysis.