Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images

Abstract

Rationale and objectives: Characterization of smoking-related lung disease typically consists of visual assessment of chest computed tomographic (CT) images for the presence and extent of emphysema and centrilobular nodularity (CN). Quantitative analysis of emphysema and CN may improve the accuracy, reproducibility, and efficiency of chest CT scoring. The purpose of this study was to develop a fully automated texture-based system for the detection and quantification of centrilobular emphysema (CLE) and CN in chest CT images.

Materials and methods: A novel approach was used to prepare regions of interest (ROIs) within the lung parenchyma for representation by texture features associated with the gray-level run-length and gray-level gap-length methods. These texture features were used to train a multiple logistic regression classifier to discriminate between normal lung tissue, CN or "smoker's lung," and CLE. This classifier was trained and evaluated on 24 and 71 chest CT scans, respectively.

Results: During training, the classifier correctly classified 89% of ROIs depicting normal lung tissue, 74% of ROIs depicting CN, and 95% of ROIs manifesting CLE. When the performance of the classifier in quantifying extent of CN and CLE was evaluated on 71 chest CT scans, 65% of ROIs in smokers without CLE were classified as CN, compared to 31% in nonsmokers (P < .001) and 28% in smokers with CLE (P < .001).

Conclusions: The texture-based framework described herein facilitates successful discrimination among normal lung tissue, CN, and CLE and can be used for the automated quantification of smoking-related lung disease.

Figure 1

Summary of proposed method for quantification of centrilobular emphysema and centrilobular nodularity. ROI, region of interest.

Figure 2

Representative regions of interest from (a) a scan of a normal subject with no emphysema, (f) a scan of a smoker with no emphysema, and (k) a scan of a subject with centrilobular emphysema binned between −1024 and −950 Hounsfield units (HU) (column 2), between −1024 and −856 HU (column 3), between −950 and −856 HU (column 4), and between the minimum and maximum gray levels in the parenchyma (column 5).

Figure 3

Results of classification are shown in three dimensions for (a) a scan of a normal subject with no emphysema, (b) a scan of a smoker with no emphysema, and (c) a scan of a subject with centrilobular emphysema (CLE). Regions of interest (ROIs) colored blue were classified as normal lung, green ROIs were found to contain centrilobular nodularity, and red ROIs were found to manifest CLE.

Figure 4

Percentage of regions of interest classified as normal lung (blue), centrilobular nodularity or “smoker’s lung” (green), and centrilobular emphysema (CLE) (red) for (a) 25 normal subjects with no emphysema, (b) 24 smokers with no emphysema, and (c) 22 subjects with CLE. Gold, Global Initiative for Chronic Obstructive Lung Disease.

Figure 5

Percentage emphysema obtained using the texture-based method is correlated with (a) percentage low-attenuation area (LAA) computed by the density mask technique and (b) chronic obstructive pulmonary disease (COPD) Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. Black data points represent scans of normal subjects with no emphysema (NNE); cyan represents scans of smokers with no emphysema (SNE); and blue, green, and red represent GOLD stages 1, 2, and 3, respectively. The differences in percentage emphysema computed using the texture-based method were statistically significant (P < .001) by analysis of variance. SNE scans were not statistically significantly different from NNE scans, but GOLD stages 1, 2, and 3 were statistically significantly different from both NNE and SNE classes (P < .05).