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 1.0. mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5. mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3. mm 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 8. min 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.
Bibliographical noteFunding Information:
This work was supported in part by grants R01 HL096613 , P50 CA090440 and P50 HL084948 from National Heart, Lung, and Blood Institute, National Institutes of Health , to the University of Pittsburgh, the SPORE in Lung Cancer Career Development Program, and the Bonnie J. Addario Lung Cancer Foundation.
- Plane fitting
- Pulmonary fissure
- Surface detection