Title
A derivative of stick filter for pulmonary fissure detection in CT images
Abstract
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.
Year
DOI
Venue
2013
10.1117/12.2006566
Proceedings of SPIE
Keywords
Field
DocType
Pulmonary fissure,image enhancement,segmentation,stick derivative
Computer vision,Perpendicular,Likelihood function,Transverse plane,Segmentation,Optics,Connected component,Curvilinear coordinates,Artificial intelligence,Fissure,Sagittal plane,Physics
Conference
Volume
Issue
ISSN
8669
null
0277-786X
Citations 
PageRank 
References 
2
0.37
14
Authors
5
Name
Order
Citations
PageRank
Changyan Xiao1133.00
Marius Staring297159.25
Juan Wang320.37
Denis Shamonin4664.47
Berend C Stoel519911.58