摘要
Bhanu图像分割模型发挥了遗传算法在处理复杂数据上的优势,但存在一定缺陷,容易被次要信息影响分割效果。为了克服这个缺点,首先介绍Bhanu模型的过程,在此基础上提出了将特征参数权值作为优化对像,利用遗传算法对Bhanu模型进行了改进,阐述了改进模型的自动学习过程,最后提出了将改进模型应用于阵发性心脏病(PAF)的分类判别中的解决方案。通过对实验数据的分析,论证了改进方案的可行性和有效性。
Bhanu Image Segmentation Model takes advantages of the genetic algorithm in solving complex data, but with some defects such as being easily affected its segmentation effect by secondary information. To overcome this shortcoming, firstly the process of Bhanu Model is introduced, and on this basis, it proposes to make the characteristic parameter weights as optimal object for improving Bhanu Model with GA, and the auto-learning process of the improved model is expatiated. At last a proposal is made to apply the improved model in solutions of Paroxysmal Heart Disease （PAF） classified discrimination. By the analysis of experimental data, it demonstrates the feasibility and effectiveness of the improved programme.
出处
《计算机应用与软件》
CSCD
2009年第11期154-156,214,共4页
Computer Applications and Software
关键词
图像分割
Bhanu模型
分类优化
遗传算法
阵发性心脏病
Image segmentation Bhanu model Classified optimization Genetic algorithm Paroxysmal heart disease
