Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT

The automatic recognition of cavity imaging signs in lung computed tomography (CT) images is of great importance for early diagnosis and possible cure of lung tuberculosis and cancers. The performance of existing recognition methods which adopt classical technology needs to be improved accordingly. In this paper, we propose an automatic recognition method based on hybrid resampling and multi-feature fusion strategies. The hybrid resampling includes multi-receptive-field and multi-window settings: the former reduces the risk of missing small or large cavities, the latter reserves context information of multiply CT windows more compactly. For multi-feature fusion, we extract features of convolutional neural networks (CNN) and classical methods (histograms of oriented gradients (HOG) and local binary pattern (LBP)). Then we compress CNN-HOG features by principal components analysis (PCA) algorithm and combine them with LBP feature. Finally, we use the fused feature to train a support vector machine (SVM) model for improving classification performance. We evaluate our method on the cavity samples from LIDC-IDRI and LISS publicly available dataset of chest CT scans, which contains 167 cavities in 164 CT images. The experimental results show that fused feature has better discriminative capability than any single feature, and has the highest FS score (0.1472 vs 0.1136) in the group with sensitivity greater than 0.8. The proposed method is compared with the latest methods for the automatic recognition of cavity imaging sign and enables higher sensitivity than the second-best method (85% vs 70%). The experiment shows that the fusion of CNN feature and classical hand-crafted feature makes full use of the complementary information, and improves classification performance when number of samples in our application is limited.