基于粒子群优化的Otsu肺组织分割算法

孟亚州; 马瑜; 白冰; 高林爽

doi:10.3788/YJYXS20153006.1000

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基于粒子群优化的Otsu肺组织分割算法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于粒子群优化的Otsu肺组织分割算法
- Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO
- 液晶与显示 2015年30卷第6期页码：1000-1007
- 作者机构：
  
  宁夏大学研究生院,宁夏银川,750021
- 作者简介：
  
  [ "孟亚州(1991-),男,山东邹城人,硕士研究生,主要从事图像处理、模式识别方面的研究。E-mail:xingqing_myz@163.com" ]
  [ "马瑜(1974-),男,宁夏西吉人,工学博士,副教授,硕士生导师,主要从事三维医学图像可视化、模式识别等方面的研究。E-mail:mayu95@163.com" ]
  [ "白冰(1990-),男,硕士研究生,主要研究方向为图像处理。E-mail:fengfubaibing@163.com" ]
  [ "高林爽(1990-),女,硕士研究生,主要研究方向为图像处理。E-mail:1046935271@qq.com" ]
- 基金信息：
  
  宁夏回族自治区2012年科技攻关计划项目(No.2012ZYG011)()
- DOI：10.3788/YJYXS20153006.1000
  中图分类号： TP394.1
- 收稿日期：2014-12-04，
  
  修回日期：2015-01-06，
  
  网络出版日期：2015-01-06，
  
  纸质出版日期：2015-12-05
- 稿件说明：
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孟亚州, 马瑜, 白冰等. 基于粒子群优化的Otsu肺组织分割算法[J]. 液晶与显示, 2015,30(6): 1000-1007

MENG Ya-zhou, MA Yu, BAI Bing etc. Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO[J]. , 2015,30(6): 1000-1007
孟亚州, 马瑜, 白冰等. 基于粒子群优化的Otsu肺组织分割算法[J]. 液晶与显示, 2015,30(6): 1000-1007 DOI： 10.3788/YJYXS20153006.1000.

MENG Ya-zhou, MA Yu, BAI Bing etc. Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO[J]. , 2015,30(6): 1000-1007 DOI： 10.3788/YJYXS20153006.1000.

摘要

为减少肺组织分割算法的运算时间

提出了一种基于粒子群优化的Otsu肺组织改进自动分割算法。针对传统粒子群优化的二维Otsu算法中二维直方图计算量大、粒子搜索容易陷入局部最优解的缺陷

使用灰度级-梯度二维直方图减少二维直方图的计算量

并减小粒子搜索范围

采用基于粒子空间对称分布的改进粒子群获取最佳阈值。算法实现过程中利用孔洞填充算法去除背景

基于形态学操作去除噪声、修补病变区域产生的孔洞。仿真实验结果显示

本文算法对图像尺寸为512像素512像素CT图像的阈值分割时间约为0.2 s

比基于灰度级-邻域均值二维直方图的粒子群优化的Otsu算法的阈值分割速度提高了约16%。较好地实现了胸腔CT图像的肺组织自动分割

与传统算法相比较

本算法在保证分割精度的基础上分割速度明显提升。

Abstract

This paper develops an automatic segmentation method of lung image using two dimensional Otsu method based on particle swarm optimization in order to reduce operation time. Aiming at the shortage of the traditional two dimensional Otsu based on particle swarm optimization

which the calculating quantity is large and standard particle swarm algorithm is easy to fall into local optimum

an improved lung segmentation based on 2D Otsu optimized by PSO is proposed in this paper. Using the grayscale-gradient two-dimensional histogram

not only reduces the amount of histogram's calculation

but also narrows the searching area of particles. The algorithm uses the improved PSO which based on diversity of particle symmetrical distribution to search optimal threshold. In the process of algorithm

the region filling algorithm is used to remove background in order to make the threshold segmentation of lung better

and the morphology operations to remove noise and repair holes which in the target image. The threshold segmenting time in this algorithm is about 0.2 s

increased about 16% than the speed of the traditional Otsu threshold segmentation optimized by PSO

the size of the CT images is 512512 pixels in this experiment. The segmentation algorithm in this paper can segment the lung in CT image automatically

not only ensures the accuracy of the segmentation

but also improves the speed of the segmentation in comparison with conventional algorithms.

关键词

Keywords

references

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