Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter

IEEE Transactions on Medical Imaging
Changyan XiaoMarius Staring

Abstract

Pulmonary fissures are important landmarks for recognition of lung anatomy. In CT images, automatic detection of fissures is complicated by factors like intensity variability, pathological deformation and imaging noise. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Considering a typical thin curvilinear shape of fissure profiles inside 2D cross-sections, the DoS filter is presented by first defining nonlinear derivatives along a triple stick kernel in varying directions. Then, to accommodate pathological abnormality and orientational deviation, a [Formula: see text] cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the post-processing stage, our main contribution is to isolate the fissure patches from adhering clutters by introducing a branch-point removal algorithm, and a multi-threshold merging framework is employed to compensate for local intensity inhomogeneity. The performance of our method was validated in experiments with two clinical CT data sets including 55 publicly available LOLA11 scans as well as separate left and righ...Continue Reading

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Citations

Apr 1, 2017·IEEE Transactions on Bio-medical Engineering·Jun ZhangDinggang Shen
Jun 9, 2019·International Journal of Computer Assisted Radiology and Surgery·Siming BayerNishant Ravikumar
Jun 30, 2019·Journal of Medical Systems·S Anitha, T R Ganesh Babu
Jul 2, 2020·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Dieuthuy PhamChangyan Xiao

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