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11 March 2008 Airway segmentation by topology-driven local thresholding
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Abstract
We describe a method for segmenting airway trees from greyscale 3D images such as CT (Computed Tomography) scans. Our approach is based on topological analysis of sets obtained by thresholding from thick slices, i.e. sub-images consisting of a small number of consecutive slices. From each thick slice under consideration, we select all sets S obtained from that thick slice by thresholding that have simple enough topological structure. As the selection criterion, we use a simple algebraic condition involving the numbers of connected components in the intersection of the set S with every slice in the thick slice. The condition basically asserts that the intersections of S with each of the slices is small and attempts to limit the number of the branching points of S within the thick slice. The output 3D model of the airway tree is obtained as the largest connected component of the union of all selected sets, extracted from several overlapping thick slices. Experiments with a number of chest CT scans show that the method leads to promising results.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrzej Szymczak and James Vanderhyde "Airway segmentation by topology-driven local thresholding", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143D (11 March 2008); https://doi.org/10.1117/12.769488
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