乳腺X线图像肿块建模与分割  

Mass Modeling and Segmentation in Mammogram

作　　者：韩振中[1] 陈后金[1] 李居朋[1] 姚畅[1] 程琳[2] 

机构地区：[1]北京交通大学电子信息工程学院,北京100044 [2]北京大学人民医院乳腺中心,北京100044

出　　处：《北京理工大学学报》2013年第5期495-499,522,共6页Transactions of Beijing Institute of Technology

基　　金：国家自然科学基金资助项目(61271305;61201363;60972093);国家教育部高等学校博士学科点专项科研基金资助课题(20110009110001);中央高校基本科研业务费专项资金资助课题(2011JBM003;2012JBM012);北京交通大学人才基金资助课题(2012RC036)

摘　　要：提出了一种基于模型分析与均值漂移聚类的乳腺肿块分割方法.该方法根据肿块的临床特征表现建立了肿块的数学模型,并通过多重滤波实现肿块的准确定位.在此基础上,结合均值漂移算法获得的像素点集合,筛选出初始肿块.最后利用无边缘活动轮廓模型准确分割出肿块.实验采用通用的MIAS数据库进行算法性能测试,结果表明本文方法在保证较低假阳性率的同时,肿块检测真阳性率高于形态学成分分析方法.此外,本文方法分割出的肿块边界完整,可满足临床检验与诊断需求.A novel method of mass segmentation in mammogram is proposed in this paper. First, a mathematical model is presented based on the clinical features of the mass and a multi-filtering method is used to detect the mass＇ location. Then, according to the result of clustered pixels, which is got by mean shift algorithm, rough handling masses could be obtained. Finally, the active contour without edge model is applied to refine the rough-wrought masses. Experiments implemented on the public MIAS database indicate that the proposed method can achieve better true positive rate than the morphological component analysis method with low false positive rate. In addition, the proposed segmentation method could emerge complete boundary, which could meet the clinical examination and diagnosis demand.

关 键 词：乳腺X线图像 乳腺癌早期检测 肿块分割 数学建模 均值漂移 无边缘活动轮廓模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]