Liver image segmentation algorithm based on RBF confidence interval

被引：0

作者：

Sun, Yongxiong ^{[1
]}

Huang, Qiuyang ^{[2
]}

Zhang, Yu ^{[3
]}

Li, Yinghan ^{[4
]}

Liu, Lipeng ^{[1
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun

[2] College of Software, Jilin University, Changchun

[3] Public Computer Teaching and Research Center, Jilin University, Changchun

[4] College of Mechanical, Jilin University, Changchun

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 05期

关键词：

Confidence interval; Liver segmentation; RBF-CI; Regional growth;

D O I：

10.12733/jics20105532

中图分类号：

学科分类号：

摘要：

For the characteristics of liver images and the shortcomings of the traditional region growing algorithm, a liver segmentation method based on RBF-CI (RBF-Confidence Interval) is proposed. On the basis of the application of window adjusting technology and the anisotropic diffusion method, the RBF neural network learning algorithm is introduced to calculate the coefficient of the confidence interval in order to reduce users' interaction amount and realize adaptive liver image segmentation. Simulation results show that, the proposed RBF-CI region growth segmentation algorithm can achieve effective segmentation results of liver images. For the ten sets of the liver images, while our proposed segmentation algorithm result achieves an average accuracy rate of 90.86%, which indicates the segmentation is accurate. Copyright © 2015 Binary Information Press.

引用

页码：1703 / 1711

页数：8

共 16 条

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