一种基于规则的脑组织磁共振图像分割新方法  

A Novel Approach for Segmentation of MRI Brain Images

作　　者：孔俊[1] 张竞丹[1] 吕英华[1] 王建中[1] 周颜军[1] 杨志强[1] 

机构地区：[1]东北师范大学计算机学院,长春130024

出　　处：《计算机科学》2006年第2期237-241,共5页Computer Science

基　　金：国家教育部重点项目(编号:02090)资助。

摘　　要：本文将小波算法、分水岭算法及基于区域的模糊 C 均值算法相结合,提出了一种基于规则的二次分割方法实现对脑组织磁共振图像的分割。首先,采用一种基于小波的滤波器去除图像中的噪声;然后采用分水岭算法实现对图像的初始分割。为克服分水岭算法的过度分割问题,本文提出了基于区域的模糊 C 均值(RFCM)聚类算法实现对过度分割区域的合并。尽管分水岭算法存在过度分割现象,仍有一些区域分割得并不完全,尤其是在脑脊液与灰质,或灰质与白质的过渡区域。为此,本文提出一种局部区域连续性与全局信息相结合的基于规则的多阈值分割方法,对分水岭算法初始分割不完全的区域再次分割。通过对大量模拟数据和真实数据分割的实验证明了此方法的准确性和可靠性。A novel method for segmentation of brain tissues in MRI （Magnetic Resonance Imaging） images is proposed in this paper. Firstly, we de-noise the images using a versatile wavelet based filter. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, result of classical watershed algorithm on grayscale textured images such as tissue images is over-segmentation. The following procedure is a merging process for the over-segmentation regions using fuzzy clustering algorithm （Fuzzy C-Means）. But there are still some regions which are not divided completely, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. We proposed a rule-based re-segmentation processing approach to partition these regions based on the combination of local region continuity and global information. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.

关 键 词：小波去噪 基于规则的分割方法 分水岭算法 模糊C均值聚类算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP311.11[自动化与计算机技术—计算机科学与技术]