摘要
提出应用最优小波包变换对磁共振颅脑图像做分解,以各子带小波包系数的能量形成纹理特征集;并运用基于核函数的模糊C均值聚类算法(Kernel-Based Fuzzy C-means Algorithm,KFCM)对所提取到的特征集进行聚类分析,从而实现了对磁共振颅脑图像的有效分割。实验证明应用KFCM算法做分割的收敛速度和抗噪性明显优于FCM算法。
A method is proposed for segmenting MR brain images using the best wavelet packet transform,and the eigenvectors are come into using the energy of wavelet packets’ coefficients.Then the Kernel-Based Fuzzy C-means Algorithm （KFCM） is used for clustering analysis of the eigenvectors. Finally,the MR brain image is segmented effectively by our method.The experiment proves that KFCM for segmentation is more robust in convergence and anti-noise than FCM.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第12期186-188,共3页
Computer Engineering and Applications
基金
湖南省自然科学基金(the Natural Science Foundation of Hunan Province of China under Grant No.05JJ30123)
湖南省教育厅科学研究项目(the Scientific Research Program of Department of Education of Hunan Province
China under Grant No.05C246)
关键词
磁共振颅脑图像
医学图像分割
最优小波包变换
纹理特征
KFCM算法
magnetic resonance imaging of brain
medical image segmentation
the best wavelet packet transform
texture feature
KFCM algorithm
