A Novel Automatic Extraction Method of Lung Texture Tree from HRCT Images

Computed tomography (CT) is the primary imaging modality for investigation of lung function and lung diseases. High resolution CT slice images of chest contain lots of texture information, which provides powerful datasets to research computer aid-diagnosis (CAD) system. But the extraction of lung tissue textures is a challenge task. In this paper, we introduce a novel method based on level set to extract lung tissue texture tree, which is automatic and effectual. Firstly, we propose an improved implicit active contour model driven by local binary fitting energy, and the parameters are dynamic and modulated by image gradient information. Secondly, a new technique of painting background based on intensity nonlinear mapping is brought forward to remove the influence of background during the evolution of single level set function. At last, a number of contrast experiments are performed, and the results of 3D surface reconstruction show our method is efficient and powerful for the segmentation of fine lung tree texture structures.