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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Characterization of Pulmonary Nodules on Computer Tomography (CT) Scans: The Effect of Additive White Noise on Features Selection and Classification Performance
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Characterization of Pulmonary Nodules on Computer Tomography (CT) Scans: The Effect of Additive White Noise on Features Selection and Classification Performance

机译：在计算机断层扫描（CT）扫描中表征肺结节：加性白噪声对特征选择和分类性能的影响

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The goal of this project is to use computer analysis to classify small lung nodules, identified on CT, into likely benign and likely malignant categories. We compared discrete wavelet transforms (DWT) based features and a modification of classical features used and reported by others. To determine the best combination of features for classification, several intensities of white noise were added to the original images to determine the effect of such noise on classification accuracy. Two different approaches were used to determine the effect of noise: in the first method the best features for classification of nodules on the original image were retained as noise was added. In the second approach, we recalculated the results to reselect the best classification features for each particular level of added noise. The CT images are from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI). For this study, nodules were extracted in window frames of three sizes. Malignant nodules were cytologically or histogically diagnosed, while benign had two-year follow-up. A linear discriminant analysis with Fisher criterion (FLDA) approach was used for feature selection and classification, and decision matrix for matched sample to compare the classification accuracy. The initial features mode revealed sensitivity to both the amount of noise and the size of window frame. The recalculated feature mode proved more robust to noise with no change in terms of classification accuracy. This indicates that the best features for computer classification of lung nodules will differ with noise, and, therefore, with exposure.
机译：该项目的目标是使用计算机分析将CT上发现的小肺结节分类为可能的良性和可能的​​恶性类别。我们比较了基于离散小波变换（DWT）的特征以及其他人使用和报告的经典特征的修改。为了确定分类特征的最佳组合，将几种强度的白噪声添加到原始图像中，以确定这种噪声对分类精度的影响。两种不同的方法用于确定噪声的影响：在第一种方法中，当添加噪声时，保留了对原始图像上的结节进行分类的最佳功能。在第二种方法中，我们重新计算了结果，以针对每个特定级别的附加噪声重新选择最佳分类功能。 CT图像来自美国国家癌症研究所（NCI）的国家肺部筛查试验（NLST）。对于本研究，在三种大小的窗框中提取了结节。通过细胞学或组织学诊断为恶性结节，而良性则为期两年。使用费舍尔准则（FLDA）方法进行线性判别分析，以进行特征选择和分类，并使用匹配样本的决策矩阵来比较分类准确性。初始特征模式显示出对噪声量和窗框大小的敏感性。经重新计算的特征模式证明对噪声更强健，而分类准确度没有变化。这表明对肺结节进行计算机分类的最佳功能会因噪音而有所不同，因此，随暴露时间也会有所不同。

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