Identification of pulmonary fissures using a piecewise plane fitting algorithm

• Published in: Computerized medical imaging and graphics Vol. 36; no. 7; pp. 560 - 571
• Main Authors: Gu, Suicheng, Wilson, David, Wang, Zhimin, Bigbee, William L., Siegfried, Jill, Gur, David, Pu, Jiantao
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2012
• Subjects: Algorithms; Clustering; Humans; Internal Medicine; Lung - anatomy & histology; Lung - diagnostic imaging; Lung Diseases - diagnosis; Lung Diseases - diagnostic imaging; Other; Pattern Recognition, Automated - methods; Plane fitting; Pulmonary fissure; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted - methods; Segmentation; Surface detection; Tomography, X-Ray Computed - methods; Pulmonary fissure; Surface detection; Plane fitting; Segmentation; Clustering
• ISSN: 0895-6111; 1879-0771; 1879-0771
• DOI: 10.1016/j.compmedimag.2012.06.001

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created “reference standard” and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.