Quantitative pathologic analysis of pulmonary nodules using three-dimensional computed tomography images based on latent Dirichlet allocation

The main purpose of this paper is to quantificationally predict the pathologic characteristics of pulmonary nodules using a novel and effective computer assisted diagnosis (CADx) scheme based on latent Dirichlet allocation (LDA) model. To make use of LDA model, we propose a novel 3D rotation invariant LBP feature to construct image words through the K-means algorithm from 3D pulmonary nodule slices. A topic distribution for each pulmonary nodule can be acquired by well-trained LDA model, which was used for pathologic analysis based on rank-based statistical analysis. Using the LIDC/IDRI database, this study made experiments based on different parameters, including topic number and size of vocabulary. Experiments demonstrate that the performance of all the characteristics reached to accuracies of more than 80%. Especially, this study obtained an accuracy of 84.2% with the root mean square error (RMSE) of 1.068 on quantitative assessment of malignancy likelihood. Compared with the latest study of multi-task convolutional neutral network regression, the proposed method can obtain more accurate results of characteristic prediction of a pulmonary nodule.