Abstract
As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median F1-score of 0.8916, which was much better than conventional methods.
Highlights
Human lungs consist of five parts, separated by pulmonary fissures
The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset
In CT images, pulmonary fissures often appear as bright thin curvilinear shapes in 2D space or plate-like structures in 3D space [1]
Summary
Human lungs consist of five parts, separated by pulmonary fissures. In CT images, pulmonary fissures often appear as bright thin curvilinear shapes in 2D space or plate-like structures in 3D space [1]. Xiao et al [13] proposed a derivative of stick filter (DoS) to enhance pulmonary fissures and designed a post-processing pipeline for pulmonary fissure segmentation. This approach utilized only the magnitude information in CT images, Algorithms. In contrast to the existing work, a multiple section model with multiple planes [14]cutting presented a novel integrating magnitude and orientation information through the approach This was expected to segment pulmonary fissures. Presented a computational geometry model to extract pulmonary fissures Their an improved orientation partition scheme was developed to separate the plate-like fissure surface from later improvement [16]. Model could deviate from the abnormality of fissures in real images, resulting in missed detection
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
