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Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Author

Listed:
  • Yaqi Huang

    (Capital Medical University
    Capital Medical University)

  • Ge Hu

    (Capital Medical University
    Capital Medical University)

  • Changjin Ji

    (Capital Medical University
    Capital Medical University)

  • Huahui Xiong

    (Capital Medical University
    Capital Medical University)

Abstract

Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.

Suggested Citation

  • Yaqi Huang & Ge Hu & Changjin Ji & Huahui Xiong, 2020. "Glass-cutting medical images via a mechanical image segmentation method based on crack propagation," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19392-7
    DOI: 10.1038/s41467-020-19392-7
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