低秩和主动形状模型的肺区分割  

Lung segmentation by active shape model approach based on low rank theory

作　　者：孙申申[1] 田丹[1] 吴微[1] 康雁[2] 赵宏 Sun Shenshen;Tian Dan;Wu Wei;Kang Yan;Zhao Hong(Information and Engineering School,Shenyang University,Shenyang 110044,China;Sino-Dutch Biomedical and Information Engineering School,Northeastern University,Shenyang 110016,China)

机构地区：[1]沈阳大学信息工程学院,沈阳110044 [2]东北大学中荷生物医学与信息工程学院,沈阳110016

出　　处：《中国图象图形学报》2020年第4期759-767,共9页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61703285);中国博士后科学基金面上项目(2019M651142);辽宁省自然科学基金指导计划项目(20180550615);沈阳市科技计划项目(18013015)。

摘　　要：目的肺区分割是肺癌计算机辅助诊断系统的首要步骤。主动形状模型(active shape model,ASM)能根据训练集获得肺区形状模型,再结合待分割肺区影像自身的局部特征,进行测试影像的分割。由于主成分分析(principal component analysis,PCA)仅能去除服从高斯分布的噪声,不能处理其他类型的噪声,所以当训练集含有非高斯类型的噪声样本时,采用基于PCA的ASM无法训练出正确的形状模型,使得肺区分割不能得到正确的结果。而低秩(low rank,LR)理论的鲁棒主成分分析(robust principal component analysis,RPCA)能去除各种类型的噪声,基于此,本文提出一种将RPCA与ASM相结合的方法。方法首先对训练样本集标记点矩阵进行低秩分解,去除噪声样本对训练出的形状模型的影响。然后在ASM训练局部梯度模型时,用判断训练样本轮廓上的标记点曲率直方图的相似度来去除噪声样本。结果在训练集含噪声样本时,将基于RPCA的ASM与传统ASM(即基于PCA的ASM)分别生成的形状模型进行对比,发现基于RPCA的ASM生成的形状模型与训练集无噪声样本时传统ASM生成的形状模型更相符。在训练集含噪声样本的情况下,基于RPCA的ASM方法分割EMPIRE10数据集中的22个肺影像,与金标准的重叠度为94.5%,而基于PCA的ASM方法分割准确率仅为69.5%。结论实验结果表明,在训练样本集中有噪声样本的情况下,基于RPCA的ASM分割能得到比基于PCA的ASM更好的分割效果。Objective Computer-aided diagnosis of lung cancer is the automatic recognition of lung lesions in computed tomography( CT) images through a computer image processing technology.Given that lung lesions are located in the lung areas,the contours of lung areas should be automatically marked on lung images first.Thus,lung segmentation is the first step in the computer-aided diagnosis of lung cancer.Current segmentation methods for pulmonary parenchyma,including normal and pathological,can be divided into three categories according to visual appearance,location relationship of shape,and whether or not the method is a hybrid.Methods based on visual appearance mostly consider the lower gray values of lung areas instead of surrounding tissues.Therefore,a pathological lung( especially including interstitial lung disease) is segmented by a classifier on the basis of gray and texture features.These methods mainly introduce the anatomical relationship of the lungs with the heart,liver,spleen,and rib.Hybrid methods establishes shape models and evolves lung contours according to surface features.They can achieve good segmentation results without noise samples in training sets but does not consider the problem of noise samples in the training sets during the construction of training shape models.The active shape model( ASM) based on principal component analysis( PCA) is an outstanding method,which can only deal with Gaussian noise.ASM is a statistical deformation model and uses PCA to obtain the average shape and the allowable range of shape of a training sample set and then establishes a shape model.ASM establishes a local feature appearance model according to the training sample set.Finally,ASM evolves the contour of the test image on the basis of the local feature appearance and shape models.Robust PCA( RPCA) in low rank theory is robust to any kind of noise.In addition,the shapes of a training sample set have a low rank attribute because the anatomical shapes of the human lungs are roughly the same.Therefore,this study comb

关 键 词：低秩 主动形状模型 鲁棒主成分分析 肺区分割 噪声样本 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]