Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (4): 470-474.DOI: 10.12068/j.issn.1005-3026.2020.04.003

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Fast Segmentation Algorithm of 3D Lung Parenchyma Based on Continuous Max-Flow

ZHAO Hai, ZHOU Bing-ling, ZHU Hong-bo, DOU Sheng-chang   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2019-05-28 Revised:2019-05-28 Online:2020-04-15 Published:2020-04-17
  • Contact: ZHOU Bing-ling
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Abstract: Pulmonary nodules are the main form of lung cancer, which has complex shape and structure and is easy to adhere to normal tissues, making it difficult to segment. A three-dimensional lung parenchyma segmentation method based on spatial constraints was proposed to achieve segmentation of lung parenchyma tissue and acquisition of target regions.First, the SLIC method was used to construct a two-dimensional CT sequence image into a superpixel image matrix, and the matrix was thinned to reduce the dimension of the matrix. Then the nodes between adjacent slices were connected to construct a three-dimensional structure of the lung parenchyma. Finally, the continuous max-flow method was used to segment the constructed three-dimensional lung structure. The experimental results showed that the proposed algorithm can quickly and accurately segment three-dimensional lung parenchyma tissue, and obtain good results for segmentation of different types of lung nodules, which has certain clinical application value.

Key words: segmentation of lung parenchyma, space constraint, continuous max-flow, energy function, sparse matrix

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