Automated 3-D Tissue Segmentation Via Clustering

  • Samuel Edwards Computational Sciences Division, U.S. Army Research Laboratory
  • Scott Brown Computational Sciences Division, U.S. Army Research Laboratory
  • Michael Lee Computational Sciences Division, U.S. Army Research Laboratory
Keywords: medical images, magnetic resonance imaging, computer-assisted tomography, clustering, DBSCAN

Abstract

Generation of 3-D tissue models from medical imagery is useful for surgical planning and computer simulations, but often requires some amount of manual effort. In this work, we use the clustering algorithm, DBSCAN, in concert with a 3-D buildup procedure to automatically generate 3-D surface models of the brain and lungs from computed tomography head and chest scans, respectively. Extensions to other tissue types such as heart and liver are demonstrated for contrast-enhanced imagery.

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Published
2018-05-03
How to Cite
Edwards, S., Brown, S., & Lee, M. (2018). Automated 3-D Tissue Segmentation Via Clustering. Journal of Biomedical Engineering and Medical Imaging, 5(2), 08. https://doi.org/10.14738/jbemi.52.4204