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Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
Author to whom correspondence should be addressed.
Algorithms 2019, 12(4), 75;
Received: 26 February 2019 / Revised: 2 April 2019 / Accepted: 8 April 2019 / Published: 15 April 2019
PDF [8631 KB, uploaded 15 April 2019]


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. View Full-Text
Keywords: CT image; pulmonary fissure detection; image segmentation; fissure enhancement CT image; pulmonary fissure detection; image segmentation; fissure enhancement

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Xiao, R.; Zhou, J. Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model. Algorithms 2019, 12, 75.

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