Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 240-243.doi: 10.11896/j.issn.1002-137X.2017.6A.055

Previous Articles     Next Articles

New Method for Medical Image Segmentation Based on BP Neural Network

TANG Si-yuan, XING Jun-feng and YANG Min   

  • Online:2017-12-01 Published:2018-12-01

Abstract: For the medical image segmentation,good accuracy of results is very important and helpful for doctors to diag-nose the illness and make the right therapeutic schemes.The traditional BP neural network is used to segment medical image,but is sensitive to the initial weights,and it has fixed learning rate,slow convergence and is easy to fall into local minimum that.A method for medical image segmentation of BP neural network based on improved particle swarm optimization algorithm was proposed.Firstly,the mapping relationships are used to algorithm of particle swarm optimization algorithm and the BP neural network.The best adaptive functions can be found by particle swarm of powerful search function,which make the BP neural network attain the minimal error.It can overcome running into local minimum value easily in BP neural network.Secondly,the best position of particles can be determined,the most reasonable weights and bias values of BP neural network are obtained and network convergence speed is improved etc.Lastly,the BP neural network are repeatedly trained,then the best output values are obtained and the threshold values are calculated,The image area is divided by threshold.The simulation results show that the use of improved algorithm for medical images segmentation,can get more clear effect of segmentation image,improve the segmentation accurate rate,and it is important for the clinical diagnosis.

Key words: Medical image segmentation,Neural network,Particle swarm optimization algorithm,Adaptive functions,Mean square error

[1] 章毓晋.图像分割[M].北京:科学出版社,2011.
[2] 严加勇,庄天戈.医学超声图像分割技术的研究及发展趋势[J].北京生物医学工程,2013,2(1):67-71.
[3] 董建明,胡觉亮.基于PSO算法的图像分割方法[J].计算机工程与技术,2006,7(18):77-78.
[4] 葛哲学,孙志强.神经网络理论与MATLAB R2007实现[M].北京:电子工业出版社,2007.
[5] WU Y T,DAI Y M,JI Z C,et al.Hybrid particle swarm optimization algorithm with cooperation of multiple particle roles[J].Journal of Computer Applications,2014,34(8):2306-2310.
[6] 吴逸庭,戴月明,纪志成,等.多粒子角色协同作用的混合粒子群优化算法[J].计算机应用,2014,4(8):2306-2310.
[7] YOU J X,CHEN J L,DONG M G.An improved Multistage multi-objective particle swarm Optimization algorithm[J].Journal of Chinese Computer Systems,2015,6(4):792-796.
[8] 林晓梅,吕姗姗,等.基于神经网络-粒子群优化算法的医学图像分割新方法[J].长春工业大学学报,2013,9(2):158-161.
[9] 吴启迪,汪镭.智能微粒群研究及应用[M].南京:江苏教育出版社,2005.
[10] 张煜东,吴乐南,吴含,等.工程优化问题中神经网络与进化算法的比较[J].计算机工程与应用,2015,5(3):1-6.
[11] 马义德,齐春亮.基于遗传算法的脉冲耦合神经网络自动系统的研究[J].系统仿真学报,2011,8(3):722-725.
[12] 田娅,饶妮妮,蒲立新.国内医学图像处理技术的最新动态[J].电子科技大学学报,2012,1(5):485-489.
[13] 朱学芳.计算机图像处理导论[M].北京:科学技术文献出版社,2013:201-211.
[14] 阮秋琦.数字图像处理[M].北京:电子工业出版社,2010:169-190.
[15] 王亮申,欧宗瑛.图像纹理分析的灰度-基元共生矩阵法[J].计算机工程,2014,23(30):19-21.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!