Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.

Journal: BioMed research international
Published Date:

Abstract

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.

Authors

  • Shi Qiu
    Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
  • Jingtao Sun
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
  • Tao Zhou
    Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Guilong Gao
    Key Laboratory of Ultra-Fast Photoelectric, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences Xi'an, 710119, China.
  • Zhenan He
    Shaanxi Institute of Medical Device Quality Supervision and Inspection, Xi'an 712046, China.
  • Ting Liang
    Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.