Deep Belief Network and Closed Polygonal Line for Lung Segmentation in Chest Radiographs

Abstract Due to the varying appearance in the upper clavicle bone region, sharp corner at the costophrenic angle, the presence of strong edges at the rib cage and clavicle and the lack of a consistent anatomical shape among different individuals, accurate segmentation of lung on chest radiographs remains challenging. In this work, we propose a novel segmentation method for lung segmentation, containing two subnetworks, where few manually delineated points are used as the approximate initialization. The first one is a preprocessing subnetwork based on a deep learning model (i.e. Deep Belief Network and K-Nearest Neighbor). The second one is a refinement subnetwork, designed to make the preprocessed result to be optimized by combining an improved principal curve method and a machine learning method. To prove the performance of the proposed method, several public datasets were evaluated with Dice Similarity Coefficient (DSC), overlap score (Ω), Sensitivity (Sen), Positive Predictive Value (PPV), global Error (E) and execution time (t). Compared with state-of-the-art methods, our method reaches superior segmentation performance.