Mass Detection in Mammogram Based on Marker-pulse Coupled Neural Networks

Mass detection in mammogram plays an important role in early breast cancer diagnosis. A novel method of mass detection in mammogram is proposed. Combined with Pulse Coupled Neural Network (PCNN) model and marker-controlled watershed method, an image slicing method based on Marker-PCNN is presented. Then the suspicious regions are extracted though the Multiple Concentric Layers (MCL) analysis. Finally, the morphological features of mass are employed to eliminate the false positive areas. The experimentation results show that the detected method is excellent and the False Positive (FP) is low. The detection correction rate reached 92.08%. Compared with the original MCL method and Morphological Component Analysis (MCA) method, the proposed method has evident advantage, especially in diagnoses of dense breast cancer.