The pulmonary lobes are the five distinct anatomic divisions of the human lungs. The physical boundaries between the lobes are called the lobar fissures. Detection of lobar fissure positions in pulmonary X-ray CT images is of increasing interest for the early detection of pathologies, and also for the regional functional analysis of the lungs. We have developed a two-step automatic method for the accurate segmentation of the three pulmonary fissures. In the first step, an approximation of the actual fissure locations is made using a 3-D watershed transform on the distance map of the segmented vasculature. Information from the anatomically labeled human airway tree is used to guide the watershed segmentation. These approximate fissure boundaries are then used to define the region of interest (ROI) for a more exact 3-D graph search to locate the fissures. Within the ROI the fissures are enhanced by computing a ridgeness measure, and this is used as the cost function for the graph search. The fissures are detected as the optimal surface within the graph defined by the cost function, which is computed by transforming the problem to the problem of finding a minimum s-t cut on a derived graph. The accuracy of the lobar borders is assessed by comparing the automatic results to manually traced lobe segments. The mean distance error between manually traced and computer detected left oblique, right oblique and right horizontal fissures is 2.3 ± 0.8 mm, 2.3 ± 0.7 mm and 1.0 ± 0.1 mm, respectively.
The human lungs are divided into five distinct anatomic compartments called lobes. The physical boundaries between the lobes are called the lobar fissures. Detection of lobar fissure positions in pulmonary X-ray CT images is of increasing interest for the diagnosis of lung disease. We have developed an automatic method for
segmentation of all five lung lobes simultaneously using a 3D watershed transform on the distance transform of a previously generated vessel mask, linearly combined with the original data. Due to the anatomically separate airway sub-trees for individual lobes,
we can accurately and automatically place seed points for the watershed segmentation based on the airway tree anatomical description, due to the fact that lower generation airway and vascular tree segments are located near each other. This, along with seed point placement using information on the spatial location of the lobes, can give a close approximation to the actual lobar fissures. The accuracy of the lobar borders is assessed by comparing the automatic segmentation to manually traced lobar boundaries. Averaged over all volumes, the RMS distance errors for the left oblique fissure, right oblique fissure and right horizontal fissure are 3.720 mm, 0.713 mm and 1.109 mm respectively.
Several methods for automatic lung segmentation in volumetric computed tomography (CT)
images have been proposed. Most methods distinguish the lung parenchyma from the surrounding
anatomy based on the difference in CT attenuation values. This can lead to an irregular and inconsistent
lung boundary for the regions near the mediastinum. This paper
presents a fully automatic method for the 3D smoothing of the lung boundary using information
from the segmented human airway tree. First, using the segmented airway tree we define a
bounding box around the mediastinum for each lung, within which all operations are performed.
We then define all generations of the airway tree distal to the right and left mainstem bronchi
to be part of the respective lungs, and exclude all other segments. Finally, we perform a fast
morphological closing with an ellipsoidal kernel to smooth the surface of the lung. This
method has been tested by processing the segmented lungs from eight normal datasets. The mean
value of the magnitude of curvature of the contours of mediastinal transverse slices, averaged
over all the datasets, is 0.0450 before smoothing and 0.0167 post smoothing. The accuracy
of the lung contours after smoothing is assessed by comparing the automatic results to manually
traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square
difference between human and computer borders is 0.8691 mm after smoothing, compared to 1.3012 mm
before. The mean similarity index, which is an area overlap measure based on the kappa statistic, is
0.9958 (SD 0.0032).