A three-dimensional approach for identifying small pulmonary vessels in smokers

False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images

BACKGROUND

Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives.

OBJECTIVE

In this study, we focus on the false positive reduction and present a method for accurate and rapid detection of pulmonary nodule from suspective regions with 3D texture and edge feature.

METHODS

This work mainly consists of four modules. Firstly, small pulmonary nodule candidates are preprocessed by a reconstruction approach for enhancing 3D image feature. Secondly, a texture feature descriptor is proposed, named cross-scale local binary patterns (CS-LBP), to extract spatial texture information. Thirdly, we design a 3D edge feature descriptor named orthogonal edge orientation histogram (ORT-EOH) to obtain spatial edge information. Finally, hierarchical support vector machines (H-SVMs) is used to classify suspective regions as either nodules or non-nodules with joint CS-LBP and ORT-EOH feature vector.

RESULTS

For the solitary solid nodule, ground-glass opacity, juxta-vascular nodule and juxta-pleural nodule, average sensitivity, average specificity and average accuracy of our method are 95.69%, 96.95% and 96.04%, respectively. The elapsed time in training and testing stage are 321.76 s and 5.69 s.

CONCLUSIONS

Our proposed method has the best performance compared with other state-of-the-art methods and is shown the improved precision of pulmonary nodule detection with computationaly low cost.

Assessing pulmonary hypertension in COPD. Is there a role for computed tomography?

Pulmonary hypertension (PH) is a common complication of chronic obstructive pulmonary disease (COPD) and is associated with increased morbidity and mortality. Reference standard method to diagnose PH is right heart catheterization. Several non-invasive imaging techniques have been employed in the detection of PH. Among them, computed tomography (CT) is the most commonly used for phenotyping and detecting complications of COPD. Several CT findings have also been described in patients with severe PH. Nevertheless, CT analysis is currently based on visual findings which can lead to reproducibility failure. Therefore, there is a need for quantification in order to assess objective criteria. In this review, progresses in automated analyses of CT parameters and their values in predicting PH and COPD outcomes are presented.

Computer-aided identification of interstitial lung disease based on computed tomography

OBJECTIVE

Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls.

MATERIALS AND METHODS

We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers.

RESULTS

Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05).

CONCLUSIONS

This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs.