聚类分析在肺结节识别中的应用  

Research on cluster analysis in pulmonary nodule recognition

作　　者：孙娟[1] 王兵[1] 杨颖[2] 田学东[1] 

机构地区：[1]河北大学数学与计算机学院,河北保定071002 [2]河北大学附属医院CT室,河北保定071000

出　　处：《计算机应用》2014年第7期2050-2053,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(10804025;61375075);河北省自然科学基金资助项目(F2012201020;F2014201098)

摘　　要：针对肺部微小结节难于识别的问题,提出用聚类算法分析肺部感兴趣区域(ROI)的方法。为进一步提高运行速度和识别率,提出全权模糊聚类算法PWFCM,给每个样本及其特征分别赋予权值并引入新的隶属度约束改进收敛性;利用二次聚类策略降低不均衡ROI数据造成的低敏感度。对实际CT影像数据进行测试,实验结果表明:该聚类分析具有高敏感度和低假阳性率,能有效地检测出肺结节。Aiming at the problem of pulmonary small nodules was difficult to identify, a method using fuzzy C-means clustering algorithm to analyse the lung Region Of Interest （ROI） was presented. An improved Fuzzy C-Means clustering algorithm based on Plurality of Weight （PWFCM） was presented to enhance the accurate rate and speed of small nodules recognition. To improve the convergence, each sample and its features were weighted and a new membership constraint was introduced. The low sensitivity from the uneven ROI data was decreased by using a double clustering strategy. The experimental results tested on the real CT image data show that PWFCM algorithm can detect lung nodules with a higher sensitivity and lower false positive rate.

关 键 词：医学图像 计算机断层扫描 感兴趣区域 肺结节 模糊C均值聚类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]