基于BP神经网络的医学图像分割新方法

摘要

对于医学图像而言,其分割结果的准确性对医生诊断病情并给出正确的治疗方案至关重要.应用传统的BP神经网络对医学图像进行分割,存在对初始权重值敏感、学习速率固定、收敛速度慢和易陷入局部极小值等问题.因此,提出了一种基于改进的粒子群优化算法的BP神经网络的医学图像分割方法.首先,应用粒子群优化算法与BP神经网络的映射关系,通过粒子群强大的搜索功能找到最佳适应函数,使对应的BP神经网络的均方误差达到最小值,克服了BP神经网络产生多个局部最小值的可能;其次,确定粒子的最佳位置后,在BP神经网络学习中获得最合理的权值和偏置值,以提高网络的收敛速度;最后,BP神经网络经反复训练后,获得最佳输出值,并计算阈值,通过阈值来分割图像区域.实验结果表明,利用改进的算法能够得到更清晰的图像分割效果,提高了图像的分割精度,对临床的诊断也具有重要参考意义.%For the medical image segmentation,good accuracy of results is very important and helpful for doctors to diagnose 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.