PSO-SVM算法在肝脏B超图像识别中的应用

为提高肝脏B超图像的诊断准确率,研究了将粒子群算法(Particle Swarm Optimization,PSO)和支持向量机(Support Vec-tor Machine,SVM)相结合进行肝脏B超图像识别的方法;该方法首先提取肝脏B超图像的空域和频域的纹理特征,然后运用SVM对108幅肝脏B超图像进行分类,利用PSO算法优化SVM的模型参数,最后将该方法与基于网格搜索法优化的SVM和基于BP神经网络的分类方法进行了对比;实验结果表明,在PSO-SVM算法下,所提取的两种纹理特征相结合能够有效地描述肝脏B超图像,基于粒子群优化算法的支持向量机模型具有较高的识别精度,平均分类准确率达94.44%,这就表明PSO-SVM算法适用于对肝脏B超图像的识别。

Application of PSO-SVM Algorithm in Liver B Ultrasound Images Recognition

To improve the diagnostic accuracy of liver B ultrasound images,an approach was proposed based on Particle Swarm Optimization(PSO) and Support Vector Machine(SVM).First,texture features of spatial and frequency domains were extracted from liver B ultrasound images.Then,SVM was used to conduct the classification of 108 liver B ultrasound images.And PSO algorithm was used to optimize the model parameters.Last,the proposed method was compared with that SVM based on grid search method and BP neural network.Experimental results show,in the PSO-SVM algorithm,the combination of two texture features that extracted can effectively describe liver B ultrasound images.And SVM model based on PSO has higher recognition accuracy with its average accuracy up to 94.44%,which confirmed PSO-SVM algorithm is suitable for the recognition of liver B ultrasound images.