科研成果详情

Accurate lung segmentation in chest radiographs is a challenging problem due to the presence of strong edges at the rib cage and clavicle, the varying appearance in the upper clavicle bone region, too small costophrenic angle and the lack of a consistent anatomical shape among different individuals. In this paper, we propose a hybrid semi-automatic method called Hull-Closed Polygonal Line Method (Hull-CPLM) to detect the boundaries of the lung Region of Interest (ROI). To the best of our knowledge, this is the first attempt at lung segmentation using the Hull-CPLM in chest radiographs. The proposed method has two main steps: 1) an image preprocessing method is constructed to implement the coarse segmentation by using as low as 15% of the manually delineated points as the initial points, 2) a refinement step is used to fine-tune the segmentation results based on the improved principal curve model and the machine learning model at the refinement step. To prove the performance of the proposed method, both the private and public databases were used. The private database is used to select the optimal parameters for the proposed method, where the result showed a good performance with the Dice Similarity Coefficient (DSC) as high as 97.08%. While on the public databases, our proposed algorithm not only surpassed the performance of different hybrid algorithms but also reached superior segmentation results by comparing with state-of-the-art methods.