Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 28-32.doi: 10.11896/jsjkx.200900176

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Lung Tissue Segmentation Algorithm:Fractional Order Sparrow Search Optimization for OTSU

JIANG Yan1, MA Yu1,2, LIANG Yuan-zhe1, WANG Yuan1, LI Guang-hao1, MA Ding1   

  1. 1 School of Physics & Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,China
    2 Academic Affairs Office of Ningxia University,Yinchuan 750021,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:JIANG Yan,born in 1994,postgradua-te.Her main research interests include pattern recognition,and image proces-sing.
    MA Yu,born in 1974,Ph.D,professor.His main research interests include computer vision,and pattern recognition.
  • Supported by:
    Ningxia Natural Science Foundation(NZ16009),Scientific Research Project of Ningxia University(NGY2016015),Research and Practice Project of Ningxia Graduate Education and Teaching Reform in 2018(YJG201811) and Graduate Innovation Research Project of Ningxia University(GIP2019060).

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Abstract: Aiming at the characteristics of slow and easy to get into local optimum for traditional particle swarm optimization used for lung tissue segmentation,a lung tissue segmentation algorithm based on fractional sparrow search optimization for OTSU is proposed.Using fractional calculus algorithm to optimize sparrow search algorithm,according to the position information of sparrow,the adaptive fractional order is introduced to adjust the fractional order adaptively and accelerate the convergence speed of the algorithm.The grayscale-gradient 2D histogram is used to reduce the computation of 2D histogram and the search range of sparrow.During the implementation of the algorithm,the hole filling algorithm is used to remove the CT image background,and morphological operation is used to remove the noise and repair the holes in the lesion area.The experiment show that the number of stable convergence times achieved by the proposed algorithm is 22.75%,13.75% and 2.25% lower than that of particle swarm optimization OTSU algorithm,fractal-order particle swarm optimization OTSU algorithm and sparrow search optimization OTSU algorithm,respectively.Therefore,the algorithm in this paper not only guarantees the segmentation accuracy,but also improves the convergence speed of the algorithm.

Key words: Adaptive, Fractional order, Hole filling, Maximum interclass variance, Sparrow search algorithm

CLC Number: 

  • TP391
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