【摘要】 目的:利用支持向量机(SVM)对乳腺X光片图像中的病变区域进行检测和分类,识别出含钙化点区域和肿瘤区域。方法:在对目标区域加特定方形窗处理后,提取直接参数、灰度共生矩阵参数和频域参数,分别作为SVM分类器的输入进行训练和测试,并与3种参数同时输入的结果进行比较。结果:单独使用直接参数,频域参数和灰度共生矩阵参数的分类结果分别是92.28%、90.35%和91.12%,而3种参数结合的结果是99.23%。结论:所提取的3种参数可以较好地反映含钙化点区域、肿瘤区域和正常区域的特征,使用SVM分类器进行分类后取得了很好的效果,基本上可以准确识别出3种区域。更多还原

【Abstract】 Objective To search an approach based on Support Vector Machine (SVM) for detection of different abnormalities including micro-calcifications and masses from digital mammograms. Methods Such detections were formulated as supervised-learning problems and SVM was applied to the detection algorithm. After the regions of interest were pre-processed by specific rectangular windows, three kinds of parameters were extracted, including the direct pixel value parameter, the parameters from Spatial Grey Level Dependency (SGLD) matrices and from Discrete Cosine Transform (DCT). At first, each kind of parameter was taken as the input of SVM to train and test the machine respectively. Then all the parameters were incorporated into the input of SVM. Results the classification accuracy is 92.28%, 90.35% and 91.12% respectively when only one parameter input. The classification accuracy reaches 99.23% when all the parameter incorporated. Conclusion The parameters extracted from the regions of interest in digital mammograms can reflect the characteristics of different regions and SVM is a powerful tool for the detection of abnormalities from digital mammograms.更多还原