论文标题
利用SLIC SUPERPIXEL分割和级联集合SVM进行乳房X线照片中的全自动质量检测
Leveraging SLIC Superpixel Segmentation and Cascaded Ensemble SVM for Fully Automated Mass Detection In Mammograms
论文作者
论文摘要
乳房X线照片中乳腺肿块的鉴定和分割面临着复杂的挑战，这是由于恶性密度高度可变的性质，其形状，轮廓，质地和方向。此外，分类器通常会在候选区域中遭受高层失衡，而正常组织区域的人数远远超过了恶性肿瘤。本文提出了一种严格的分割方法，该方法由使用灰度线性过滤器的形态学增强支持。一种新型的支持向量机（SVM）的级联集合用于有效解决类别的不平衡并提供重要的预测。对于真正的正速率（TPR）为0.35、0.69和0.82，系统分别仅生成0.1、0.5和1.0假阳性/图像（FPI）。
Identification and segmentation of breast masses in mammograms face complex challenges, owing to the highly variable nature of malignant densities with regards to their shape, contours, texture and orientation. Additionally, classifiers typically suffer from high class imbalance in region candidates, where normal tissue regions vastly outnumber malignant masses. This paper proposes a rigorous segmentation method, supported by morphological enhancement using grayscale linear filters. A novel cascaded ensemble of support vector machines (SVM) is used to effectively tackle the class imbalance and provide significant predictions. For True Positive Rate (TPR) of 0.35, 0.69 and 0.82, the system generates only 0.1, 0.5 and 1.0 False Positives/Image (FPI), respectively.
