  Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung
diseases. In CT images, pulmonary fissure segmentation is a formidable mission
due to various of factors. To address the challenge, an useful approach based
on ODoS filter and shape features is presented for pulmonary fissure
segmentation. Here, we adopt an ODoS filter by merging the orientation
information and magnitude information to highlight structure features for
fissure enhancement, which can effectively distinguish between pulmonary
fissures and clutters. Motivated by the fact that pulmonary fissures appear as
linear structures in 2D space and planar structures in 3D space in orientation
field, an orientation curvature criterion and an orientation partition scheme
are fused to separate fissure patches and other structures in different
orientation partition, which can suppress parts of clutters. Considering the
shape difference between pulmonary fissures and tubular structures in magnitude
field, a shape measure approach and a 3D skeletonization model are combined to
segment pulmonary fissures for clutters removal. When applying our scheme to 55
chest CT scans which acquired from a publicly available LOLA11 datasets, the
median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR)
respectively are 0.896, 0.109, and 0.100, which indicates that the presented
method has a satisfactory pulmonary fissure segmentation performance.
