CAJ | 학술논문

　　乳腺X线图像中的肿块检测是乳腺癌早期诊断的重要手段。该文提出了一种新的肿块检测方法。将脉冲耦合神经网络(Pulse Coupled Neural Networks, PCNN)与标记符相结合设计了标记PCNN图像分层方法，继而利用多同心层(Multiple Concentric Layers, MCL)模型得到可疑区域。最后，借助肿块的形态学特征剔除假阳性区域得到最终的肿块。实验结果表明，该文方法在保证假阳性率(False Positive Rate, FPR)的同时，肿块真阳性率(True Positive Rate, TPR)达到92.08%。同时针对东方女性致密型乳腺案例中检测结果明显优于MCL方法和MCA方法。
　　유선X선도상중적종괴검측시유선암조기진단적중요수단。해문제출료일충신적종괴검측방법。장맥충우합신경망락(Pulse Coupled Neural Networks, PCNN)여표기부상결합설계료표기PCNN도상분층방법，계이이용다동심층(Multiple Concentric Layers, MCL)모형득도가의구역。최후，차조종괴적형태학특정척제가양성구역득도최종적종괴。실험결과표명，해문방법재보증가양성솔(False Positive Rate, FPR)적동시，종괴진양성솔(True Positive Rate, TPR)체도92.08%。동시침대동방녀성치밀형유선안례중검측결과명현우우MCL방법화MCA방법。
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.