A derivative of stick filter for pulmonary fissure detection in CT images

Pulmonary fissures are important landmarks for automated recognition of lung anatomy and need to be detected as a pre-processing step. We propose a derivative of stick (DoS) filter for pulmonary fissures detection in thoracic CT scans by considering their thin curvilinear shape across multiple transverse planes. Based on a stick decomposition of a local rectangular neighborhood, a nonlinear derivative operator perpendicular to each stick is defined. Then, combining with a standard deviation of the intensity along the stick, the composed likelihood function will take a strong response to fissure-like bright lines, and tends to suppress undesired structures including large vessels, step edges and blobs. Applying the 2D filter sequentially to the sagittal, coronal and axial slices, an approximate 3D co-planar constraint is implicitly exerted through the cascaded pipeline, which helps to further eliminate non-fissure tissues. To generate a clear fissure segmentation, we adopt a connected component based post-processing scheme, combined with a branch-point finding algorithm to disconnect the residual adjacent clutters from the fissures. The performance of our filter has been verified in experiments with a 23 patients dataset, where pathologies to different extents are included. The DoS filter compared favorably with prior algorithms.