Thoracic imaging for small animals has emerged as an important tool for monitoring pulmonary disease progression
and therapy response in genetically engineered animals. Micro-CT is becoming the standard thoracic
imaging modality in small animal imaging because it can produce high-resolution images of the lung parenchyma,
vasculature, and airways. Segmentation, measurement, and visualization of the airway tree is an important step
in pulmonary image analysis. However, manual analysis of the airway tree in micro-CT images can be extremely
time-consuming since a typical dataset is usually on the order of several gigabytes in size. Automated and
semi-automated tools for micro-CT airway analysis are desirable. In this paper, we propose an automatic airway
segmentation method for in vivo micro-CT images of the murine lung and validate our method by comparing
the automatic results to manual tracing. Our method is based primarily on grayscale morphology. The results
show good visual matches between manually segmented and automatically segmented trees. The average true
positive volume fraction compared to manual analysis is 91.61%. The overall runtime for the automatic method
is on the order of 30 minutes per volume compared to several hours to a few days for manual analysis.
Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.