Journal
BIOMED RESEARCH INTERNATIONAL
Volume 2020, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2020/6619076
Keywords
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Funding
- Postdoctoral Science Foundation of China [2020M682144, 62062003]
- Natural Science Foundation of China [61561040]
- North Minzu University Research Project of Talent Introduction [2020KYQD08]
- Shanxi National Science Foundation [2020JQ-518]
- Open Project Program of the State Key Lab of CADCG [A2026]
- Ningxia Key Research and Development Project [2020BEB04022]
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The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.
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