基于混合分类的肺结节检测算法

使用最优阈值的分割方法获得肺实质,并使用C均值聚类的方法获得感兴趣区域(ROI),通过混合分类方法对ROI进行分类.在分类过程中,首先定义肺结节的两个三维特征以及相应的两条规则,进行基于规则的初始分类,再构造基于改进Mahalanobis距离的非线性分类器进行再次分类,从而进一步降低假阳性.经过混合分类处理,肺结节与血管等干扰信息得到有效的区分.实验结果表明,该算法检测肺结节具有较高的敏感性.

A Detection Algorithm Based on Hybrid Classification for Pulmonary Nodules

The approach to weight segmentation with optimum threshold values was used to get the pulmonary parenchyma,and the regions of interests(ROI) were obtained by C-means clustering algorithm and classified by hybrid method,during which two three-dimensional characteristics of pulmonary nodules and two corresponding rules were defined for regular initial classification.Then,a nonlinear classifier was constructed by improving the Mahalanobis distance vector to classify ROI again,so as to lower the false positive....