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 microcalcification 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 regions. Four textural features are extracted from the SRDM. These features are used to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal breast tissues. The three-layer backpropagation 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