计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 28-32.doi: 10.11896/jsjkx.200900176

• 图像处理&多媒体技术 • 上一篇    下一篇

基于分数阶麻雀搜索优化OTSU肺组织分割算法

江妍1, 马瑜1,2, 梁远哲1, 王原1, 李光昊1, 马鼎1   

  1. 1 宁夏大学物理与电子电气工程学院 银川750021
    2 宁夏大学教务处 银川750021
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 马瑜(mayu95@163.com)
  • 作者简介:1322415165@qq.com
  • 基金资助:
    宁夏自然科学基金(NZ16009);宁夏高等学校科学研究项目(NGY2016015);2018年宁夏研究生教育教学改革研究与实践项目(YJG201811);宁夏大学研究生创新研究项目(GIP2019060)

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).

摘要: 针对传统粒子群算法应用于肺组织分割时寻优慢且易陷入局部最优的问题,提出了一种基于分数阶麻雀搜索优化的最大类间差法(OTSU)肺组织分割算法。采用分数阶微积分算法优化麻雀搜索算法,根据麻雀的位置信息,引入自适应分数阶阶次以自适应地调整分数阶阶次,加快算法收敛速度;采用灰度级-梯度二维直方图以减小二维直方图的计算量和麻雀的搜索范围;算法实现过程中,利用孔洞填充算法去除CT图像背景,采用形态学操作去除噪音并修补病变区域产生的孔洞。实验表明,所提算法达到稳定的收敛次数相较于粒子群优化OTSU算法、分数阶粒子群优化OTSU算法、麻雀搜索优化OTSU算法分别减少了22.75%,13.75%,2.25%,因此所提算法在保证分割精度的同时,提高了算法的收敛速度。

关键词: 分数阶, 孔洞填充, 麻雀搜索算法, 自适应, 最大类间方差

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

中图分类号: 

  • TP391
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