Hidden conditional random field for lung nodule detection
Yang Liu, Zhong-Qiu Wang, Maozu Guo, Ping Li · 2014
Lung nodule detection in thin section computerized tomography (CT) images is a useful but challenging task in the development of computer aided diagnosis (CAD) system for lung cancer. In order to improve sensitivity and reduce false positive, we consider a 3D nodule as a 2D region of interest (ROI) sequence and utilize a discriminative sequence model called hidden conditional random field to capture the correlations and transitions of a nodule's ROIs on several consecutive slices. First, we use region growing and thresholding to segment lung parenchyma. Second, selective enhancement filter is employed on 2D images to get 2D ROIs and after that, we match these ROIs on consecutive images based on a simple but effective criteria to get 2D ROI sequence(3D candidate) of a nodule. Third, given these ROI sequences, hidden conditional random field is devised to classify whether some 3D candidates are nodules or not based on these sequences. The proposed system is validated on 24 patients' scans which contain 59 nodules in total from Lung Image Database Consortium (LIDC) dataset. Experimental results demonstrate that our approach achieves high sensitivity and reduces false positive significantly.