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Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description.
Medical & Biological Engineering & Computing ( IF 3.2 ) Pub Date : 2020-06-22 , DOI: 10.1007/s11517-020-02192-y
Ali Afzali 1 , Farshid Babapour Mofrad 1 , Majid Pouladian 2
Affiliation  

Statistical shape analysis of lung is a reliable alternative method for diagnosing pulmonary diseases such as tuberculosis (TB). The 2D contour-based lung shape analysis is investigated and developed using Fourier descriptors (FDs). The proposed 2D lung shape analysis is carried out in threefold: (1) represent the normal and the abnormal (i.e. pulmonary tuberculosis (PTB)) lung shape models using Fourier descriptors modeling (FDM) framework from chest X-ray (CXR) images, (2) estimate and compare the 2D inter-patient lung shape variations for the normal and abnormal lungs by applying principal component analysis (PCA) techniques, and (3) describe the optimal type of contour-based feature vectors to train a classifier in order to detect TB using one publicly available dataset—namely the Montgomery dataset. Since almost all of the previous works in lung shape analysis are content-based analysis, we proposed contour-based lung shape analysis for statistical modeling and feature description of PTB cases. The results show that the proposed approach is able to explain more than 95% of total variations in both of the normal and PTB cases using only 6 and 7 principal component modes for the right and the left lungs, respectively. In case of PTB detection, using 138 lung cases (80 normal and 58 PTB cases), we achieved the accuracy (ACC) and the area under the curve (AUC) of 82.03% and 88.75%, respectively. In comparison with existing state-of-art studies in the same dataset, the proposed approach is a very promising supplement for diagnosis of PTB disease. The method is robust and valuable for application in 2D automatic segmentation, classification, and atlas registration. Moreover, the approach could be used for any kind of pulmonary diseases.

Contour-based lung shape analysis in order to detect tuberculosis: modeling and feature description



中文翻译:

基于轮廓的肺形状分析以检测结核:建模和特征描述。

肺的统计形状分析是诊断肺部疾病(例如结核病(TB))的可靠替代方法。基于二维轮廓的肺形状分析是使用傅立叶描述符(FDs)进行研究和开发的。拟议的2D肺部形状分析可从以下三个方面进行:(1)使用胸部X射线(CXR)图像的傅里叶描述符建模(FDM)框架表示正常和异常(即肺结核(PTB))肺部形状模型, (2)通过应用主成分分析(PCA)技术估算和比较正常和异常肺部的2D患者间肺部形状变化,以及(3)描述基于轮廓的特征向量的最佳类型以按顺序训练分类器使用一个公开可用的数据集(即蒙哥马利数据集)检测结核病。由于先前在肺形状分析中几乎所有的工作都是基于内容的分析,因此我们提出了基于轮廓的肺形状分析,用于PTB病例的统计建模和特征描述。结果表明,所建议的方法能够分别使用左右肺的6种和7种主成分模式来解释正常和PTB病例中超过95%的总变异。在PTB检测的情况下,使用138例肺部病例(80例正常和58例PTB病例),我们分别获得了82.03%和88.75%的准确度(ACC)和曲线下面积(AUC)。与同一数据集中现有的最新技术相比,该方法是诊断PTB疾病的非常有前途的补充。该方法鲁棒且有价值,可用于2D自动分割,分类,和地图集注册。而且,该方法可用于任何种类的肺部疾病。

基于轮廓的肺形状分析以检测结核病:建模和特征描述

更新日期:2020-06-22
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