Title
Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter.
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 max-min cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the postprocessing 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 right lung images from 23 GLUCOLD scans of COPD patients. Compared with manually delineating interlobar boundary references, our method obtained a high segmentation accuracy with median F1-scores of 0.833, 0.885 and 0.856 for the LOLA11, left and right lung images respectively, whereas the corresponding indices for a conventional Wiemker filtering method were 0.687, 0.853 and 0.841. The good performance of our proposed method was also verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.
Year
DOI
Venue
2016
10.1109/TMI.2016.2517680
IEEE Trans. Med. Imaging
Keywords
Field
DocType
Pulmonary fissure,fissure segmentation,image enhancement,stick derivative
Kernel (linear algebra),Computer vision,Data set,Segmentation,Abnormality,Filter (signal processing),Image segmentation,Artificial intelligence,Curvilinear coordinates,Fissure,Mathematics
Journal
Volume
Issue
ISSN
35
6
1558-254X
Citations 
PageRank 
References 
4
0.42
24
Authors
6
Name
Order
Citations
PageRank
Changyan Xiao1985.01
Berend C Stoel219911.58
M Els Bakker340.76
Yuanyuan Peng440.42
Jan Stolk5332.95
Marius Staring697159.25