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Automatic Branch Decomposition for Tubular Structures

Xiong, Guanglei, Xing, Lei, Taylor, Charles
Stanford University
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3054
New: Prefer using the following doi: https://doi.org/10.54294/2bq566
Published in The Insight Journal - 2009 January - June.
Submitted by Guanglei Xiong on 2009-05-05 01:34:35.

Branches of tubular structures (vasculature, trachea, neuron, etc.) in medical images are critical for the topology of these structures. In many applications, It is very helpful to be able to decompose tubular structures and identify every individual branch. For example, quantification of geometric vascular features, registration of trachea movement due to respiration, tracing of neuron path. However, manual decomposition can be tedious, time-consuming, and subject to operator bias. In this paper, we propose a novel method to decompose tubular structures automatically and describe how to implement it in ITK framework. The input is a 2D/3D binary image that can be obtained from any segmentation techniques, as well as the junctions, which can be generated automatically from our previously contributed ITK class: itk::JunctionDetectionFilter. The output will be branches with their labels and their connection. There are only two parameters which need to be set by the user. We provide here the implementation as a ITK class: itk::BranchDecompositionFilter. Please cite the following paper if you are interested in our work. G. Xiong, C. Chen, J. Chen, Y. Xie, and L. Xing, Tracking the Motion Trajectories of Junction Structures in 4D CT Images of the Lung, Vol. 57, No. 15, pp. 4905-4930, Physics in Medicine and Biology, 2012.