基于小波图像融合算法和改进FCM聚类的MR脑部图像分割算法  

MR Brain Image Segmentation Method Based on Wavelet Transform Image Fusion Algorithm and Improved FCM Clustering

作　　者：耿艳萍 郭小英 王华夏[2] 陈磊 李雪梅 GENG Yan-ping;GUO Xiao-ying;WANG Hua-xia;CHEN Lei;LI Xue-mei(School of Software Engineering,Shanxi University,Taiyuan 030013,China;College of Automation,Northwestern Polytechnical University,Xi'an 710072,China;School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]山西大学软件学院,太原030013 [2]西北工业大学自动化学院,西安710072 [3]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机科学》2017年第12期260-265,共6页Computer Science

基　　金：国家自然科学基金青年基金项目(61603228);国家自然科学基金(61702315)资助

摘　　要：针对很多基于模糊C均值(FCM)的图像分割算法存在对噪声敏感和分割轮廓不清晰等问题,提出一种基于小波变换图像融合算法和FCM聚类算法的MR医学图像分割算法。在图像分割系统的第一阶段,利用Haar小波多分辨率特性保持像素间的空间信息;第二阶段,利用小波图像融合算法对得到的多分辨率图像和原始图像进行融合,进而增强被处理图像的清晰度并降低噪声;第三阶段,利用改进型FCM技术对所处理的图像进行分割。在BrainWeb数据集上进行实验,与现有相关算法相比,提出的算法具有较高的分割精度,且对噪声的鲁棒性比较强,处理时间也没有明显增加。Concerning the problems that many image segmentation algorithms based on fuzzy C mean(FCM)are sensitive to noise and contour segmentation is not clear,an improved algorithm based on wavelet image fusion and FCM clustering algorithm was proposed.And it is applied to MR medical image segmentation successfully.In the first stage of the image segmentation system,the Haar wavelet multi-resolution characteristics were used to maintain spatial information between pixels.In the second stage,wavelet image fusion algorithm was adopted to fuse the obtained multi-resolution image and original image,thus to enhance the clarity of processed images and to reduce noise.In the third stage,FCM technology was used for image segmentation.Experiments on BrainWeb datasets show that compared with the current algorithms,the proposed algorithm has higher segmentation accuracy and robustness to noise,and the processing time is not obviously increased.

关 键 词：MR脑部图像分割 小波图像融合 模糊C均值聚类 鲁棒性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]