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

Abstract

Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives. 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. 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. 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 321.76s and 5.69s. 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.