Characterization of pulmonary nodules on computer tomography (CT) scans: the effect of additive white noise on features selection and classification performance

Abstract

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.