摘要
针对临床医学疾病诊断对医学图像处理精度的要求日益提高,单独使用支持向量机方法的处理结果难以满足实际需要,在此提出了一种CV模型与支持向量机相结合的C SVM医学图像分割方法,并分别给出2种分类方法的分割结果。实验表明,使用C SVM方法分割后得到的图像的边缘和细节特征更加突出,符合医学图像分割对于高精度的要求。对比2种方法的分割效果得出结论:该方法适用于磁共振医学图像分割领域,并能取得良好的效果,便于临床医学疾病的诊断。
For the increasing accuracy demand of medical image processing in clinical medicine disease diagnosis area, the support vector machine method is difficult to meet the actual needs. A new method called as C-SVM medical image segmentation method which combines CV model with support vector machine method is proposed in this paper. The segmentation results of the two methods are given respectively. The experimental results show that C-SVM method can get more prominent features of edges and details, meet the accuracy requirements of the medical image segmentation. The conclusions that this method is applicable in the field of MR medical image segmentation and convenient for the diagnosis of clinical medicine diseases were obtained from comparison effect of two segmentation methods.
出处
《现代电子技术》
2013年第4期47-50,共4页
Modern Electronics Technique
基金
国家"863"计划资助项目(2010AA09Z205)
