摘要
针对圆点滤波器不能区分粘连血管型结节、血管端点和血管交叉结构,造成假阳率高的问题,提出基于改进遗传算法封装模型的特征选择算法,并把最优特征组合输入到支持向量机分类器,该分离器能做到检测肺结节时漏检率低同时降低假阳率。选出七个特征(其中包含两种新提出的特征)作为最优特征组合。用含有肺结节的CT影像数据库(50个结节和961个假阳)测试分类器的性能,得到敏感性100%和特异性95.5%的效果。实验结果表明,该框架和算法能应用到临床中来提高影像科医生的阅片效率。改进的遗传算法比传统的遗传算法能搜索到更优的特征组合。
To solve the problem that the vascular adhesion nodule and vascular-crossing could not be distinguished by dot filter and make high rate of false positive,a feature subset selection method based on improved genetic algorithms in wrapper model was proposed,and the best feature subset was used to establish a classifier based on support vector machines to improve the performance by reducing false positive and retaining true nodule.From 22 features（including three newly proposed features） which were calculated for each detected structure,seven features were selected as the optimal feature subset.And the classifier trained with the optimal feature subset resulted in 100% sensitivity and 95.5% specificity from the lung nodule database（50 true nodules and 961 false ones）.Experiments show that the framework and the approach can be applied in clinic and ease the workload of the radiologist in interpreting lung CT scans;at the same time,the same method can also be applied to other computer-aided detection fields such as detection of mammary tumor.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2011年第3期497-501,566,共6页
Journal of System Simulation
基金
国家自然科学基金(60671050)
关键词
肺结节检测
遗传算法
支持向量机
特征选择
粘连血管型结节
lung nodule detection
genetic algorithms
support vector machines
feature selection
vascular adhesion nodule
