Identification of pulmonary fissures using a piecewise plane fitting algorithm

Abstract

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 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.

Fig. 1

Basic steps of the pulmonary fissure segmentation scheme: (a) a chest CT examination, (b) the segmented lung volume of (a), (c) the subdivision of the segmented lung volume, (d) application of a point filtering, (e) fissure detection after application of the analytical plane fitting algorithm, (f) the clustered types of fissures, (g) identified fissures displayed as overlay, and (h) the 3D surface model of the detected fissures in (g).

Fig. 2

Illustration of the plane fitting algorithm.(a)–(b) show the fitted lines in a two-dimensional point set. The lines in green represent the results obtained by the least square fitting method, and the lines in red represents the results obtained by the proposed density fitting method. (c) shows a 3D point cloud in a spherical volume that is extracted from an actual chest CT examination. (d) visualizes the point cloud in (c) using the inner product of the points vectors and the normal vector (i.e., Eq. (7)). (e) shows the fitted plane (in red) using the developed method. (f) shows the function ε(α, θ) corresponding to the 3D point cloud in (c).

Fig. 3

Fissure detection and classification results: (a) a CT examination, (b) the detected, sorted, and classified fissures (overlay with different colors), and (c) the final fissure segmentation and classification (overlay) after the removal of non-fissure regions as indicated by the arrow in (b), where the voxels in green, red and blue indicate the left oblique, right oblique and right horizontal fissures, respectively. (d) visualizes the detected fissure voxels and their classification in 3D space.

Fig. 4

The cumulative error distance distribution (CEDD) between the proposed fitting scheme/the C-Method [7] and the manually created reference standard. (F: the developed scheme, C: the C-Method [7], G: the reference standard)

Fig. 5

An example demonstrating the performance of the newly developed scheme for identifying pulmonary fissures depicted in a relatively normal examination. The top row shows the original CT images, and the bottom row shows the identified fissures in overlay. The left, middle, and right columns show the axial, the sagittal, and the coronal views, respectively.

Fig. 6

An example demonstrating the performance of the newly developed scheme, in comparison with the C-Method [7], for identifying pulmonary fissures depicted in a diseased examination with ILD (Interstitial Lung Disease). The top row shows the original CT images, the middle row shows the fissures identified by the proposed scheme, and the bottom row shows the fissures identified by the C-Method [7]. The left, middle, and right columns show the axial, the sagittal, and the coronal views, respectively.

Fig. 7

An example showing the detected pulmonary fissures by the proposed scheme in an abnormal examination with severe bronchiectasis (cystic fibrosis). The top row shows the original CT images, and the bottom row shows the identified fissures in overlay. The left, middle, and right columns show the axial, the sagittal, and the coronal views, respectively.

Fig. 8

Two examples with large discrepancies between the results obtained by the proposed computerized scheme and the reference standard. The left column shows the original CT images, the middle column shows the fissures in overlay in the reference standard, and the right column shows the fissures in overlay identified by the computerized scheme in this study. The top row shows a CT examination where portion of the right horizontal fissure voxels is invisible but identified by the fitting scheme; the “F to G” discrepancy is 26.3 mm. The bottom row shows a CT examination with the presence of pneumonia where the potions of the fissures were missed by the fitting scheme; the “G to F” discrepancy is 35.6 mm.

Fig. 9

An example showing a miss-classification of pulmonary fissures. The regions in pink indicate the discarded fissures. Red arrow indicates the true right horizontal fissure and green arrow indicates the cluster that was falsely recognized as the right horizontal fissure.