摘要目的:结合图像自动分割技术和机器学习方法对乳腺钼靶X线图像进行准确分类识别。方法:以数字钼靶X线图像数据库（DDSM）中的BI-RADS4类的簇状分布多形性钙化钼靶图像为研究对象，自动切分图像的感兴趣区域（ROI）。对小波变换、Gabor滤波和灰度共生矩阵法所提取的特征参数进行融合，并基于灵敏度分析对融合后的特征参数进行筛选。使用基于集成学习的方法，对多项式核支持向量机（SVM）、随机森林和逻辑（logistic）回归分类器进行投票集成，构成用于乳腺钼靶X线图像自动分类的分类器。投票集成方法为软投票。结果:提出的集成分类器可高效地识别与分类乳腺钼靶X线图像，其分类的灵敏度、特异度和准确率分别为99.1%、99.6%和99.3%。结论:所提出的乳腺钼靶X线图像处理与分类识别方法能为医生的临床判断提供辅助检测的依据，并为细分BI-RADS4类图像提供技术基础。

abstractsObjective:To combine automatic image segmentation technology and machine learning methods to accurately classify and recognize mammography images.Methods:Taking mammography images with clustered pleomorphic calcification as the research object, which were in BI-RADS4 class from the Digital Mammogram Database (DDSM). The region of interest (ROI) of the images was automatically segmented. The characteristic features extracted by wavelet transform, Gabor filter and gray level co-occurrence matrix method were fused. The fused feature parameters were screened based on sensitivity analysis. Using ensemble learning method, the polynomial kernel SVM, random forest and logistic regression classifiers were integrated to form a classifier for automatic classification of mammography images. The ensemble learning method was soft voting integration.Results:The proposed ensemble classifier can efficiently recognize and classify mammography images, and its classification sensitivity, specificity and accuracy on the training set were 99.1%, 99.6% and 99.3%, respectively.Conclusions:The proposed mammography image processing, classification and recognition method can provide assistant detection basis for doctors' clinical judgment, and provide a technical basis for subdividing BI-RADS4 class images.