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