Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO

MENG Ya-zhou; MA Yu; BAI Bing; GAO Lin-shuang

doi:10.3788/YJYXS20153006.1000

您当前的位置：

首页 >

文章列表页 >

Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Improved lung segmentation algorithm based on 2 D Otsu optimized by PSO
- Chinese Journal of Liquid Crystals and Displays Vol. 30, Issue 6, Pages: 1000-1007(2015)
- 作者机构：
  
  宁夏大学研究生院,宁夏银川,750021
- 作者简介：
- 基金信息：
  
  Supported by Key Science and Technology Program of Ningxia Hui Autonomous Region, Ch
- DOI：10.3788/YJYXS20153006.1000
  CLC： TP394.1
- Received：04 December 2014，
  
  Revised：06 January 2015，
  
  Published Online：06 January 2015，
  
  Published：05 December 2015
- 稿件说明：
移动端阅览
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

孟琭,贾同,赵大哲,等.基于Snake模型的病变CT肺部图像分割[J].系统仿真学报, 2009,21(15):4603-4606,4612. Meng L, Jia T, Zhao D Z, et al. Pathological lung segmentation for CT images based on Snake model[J]. Journal of System Simulation, 2009, 21(15):4603-4606, 4612. (in Chinese)

Ivanovska T, Hegenscheid K, Laqua R, et al. A fast and accurate automatic lung segmentation and volumetry method for MR data used in epidemiological studies[J]. Computerized Medical Imaging and Graphics, 2012, 36(4):281-293.

Wei Y, Shen G, Li J J. A fully automatic method for lung parenchyma segmentation and repairing[J]. Journal of Digital Imaging, 2013, 26(3):483-495.

Darmanayagam S E, Harichandran K N, Cyril S R R, et al. A novel supervised approach for segmentation of lung parenchyma from chest CT for computer-aided diagnosis[J]. Journal of Digital Imaging, 2013, 26(3):496-509.

Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on System, Man and Cybernetic, 1979, 9(1):62-66.

刘健庄,栗文青.灰度图像的二维Otsu自动阈值分割法[J].自动化学报, 1993,19(1):101-105. Liu J Z, Li W Q. The automatic thresholding of gray-level pictures via two-dimensional OTSU method[J]. Acta Automatica Sinica, 1993, 19(1):101-105. (in Chinese)

吴成茂,田小平,谭铁牛.二维Otsu法阈值法的分割快速迭代算法[J].模式识别与人工智能, 2008, 21(6):746-757. Wu C M, Tian X P, Tan T N. Fast iterative algorithm for two-dimensional Otsu Thresholding method[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(6):746-757. (in Chinese)

黄岗,李俊,潘金贵.基于粒子群优化方法的2维Otsu快速图像分割算法[J].中国图象图形学报,2011,16(3):377-381. Huang G, Li J, Pan J G. A fast 2D Otsu image segmentation algorithm based on particle swam optimization algorithm[J]. Journal of Image and Graphics, 2011, 16(3):377-381. (in Chinese)

Kennedy J, Eberthart R C. Particle Swarm Optimization[C]. Proc. IEEE Int. Conf. on Neural Networks, Piscataway:IEEE, 1995:1942-1948.

王洪涛,李丹.基于改进粒子群算法的图像边缘检测研究[J].液晶与显示, 2014,29(5):800-804. Wang H T, Li D. Image edge detection research based on improved particle swarm optimization algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(5):800-804. (in Chinese)

孙越泓.基于粒子群优化算法的图像分割研究[D].南京:南京理工大学,2011. Sun Y H. Image segmentation research based on particle swarm optimization algorithm[D]. Nanjing:Nanjing University of Science & Technology, 2011. (in Chinese)

孙越泓,魏建香,夏德深.一种基于粒子对称分布多样性的PSO算法[J].模式识别与人工智能, 2010,23(2):137-143. Sun Y H, Wei J X, Xia D S. An improved PSO based on diversity of particle symmetrical distribution[J]. Pattern Recognition and Artificial Intelligence, 2010, 23(2):137-143. (in Chinese)

Helen R, Kamaraj N, Selvi K, et al. Segmentation of pulmonary parenchyma in CT lung images based on 2D Otsu optimized by PSO[C]. Proceedings of 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), Tamil Nadu:IEEE, 2011:536-541.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰