基于模糊聚类水平集的医学图像分割方法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 155-159.

基于模糊聚类水平集的医学图像分割方法

吴杰,朱家明,陈静

扬州大学信息工程学院扬州225127,扬州大学信息工程学院扬州225127,扬州大学信息工程学院扬州225127

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金资助

Fuzzy Clustering Level Set Based Medical Image Segmentation Method

WU Jie, ZHU Jia-ming and CHEN Jing

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 医学图像分割是图像分割的一个重要应用领域,医学图像普遍存在高噪声、伪影、低对比度、灰度不均匀、不同软组织之间与病灶之间边界模糊等特点,因此运用聚类算法,结合李春明模型(LCM)和两相水平集分割方法(CV),首先选用合适的滤波器对医学图像进行去噪,然后使用模糊C均值算法(FCM)获得图像的先验模型；并对传统的CV模型进行改进,对图像进行细分割。实验表明,该模型可以解决图像高噪声、弱边界问题,并可以有效避免重新初始化,对边缘更加敏感,可提高分割精度,有效的抑制噪声,明显的减少迭代次数和时间,具有一定应用价值。。

关键词: 模糊C均值聚类,滤波器,LCM模型,FCM-LCMCV水平集方法

Abstract: Medical image segmentation is an important application field of image segmentation,it widespreadly has high noise,artifacts,low contrast,uneven gray,fuzzy boundaries between different between soft tissue lesions and other characteristics,this paper used clustering algorithm,combined with LCM and two phase model level set method (CV),chose the appropriate filter for medical image denoising,then used the fuzzy c-means algorithm to get image prior model.And we improved the traditional CV model to fine the image segmentation.Experiments show that the model can solve the problem of high image noise and weak boundary,and can effectively avoid the re-initialization,and is more sensitive to the edge,improving the segmentation accuracy,suppressing noise effectively,significantly reducing the number of iterations and time,having certain application value.

Key words: Fuzzy c-means clustering,Filter,LCM model,FCM-CMCV level set method

吴杰,朱家明,陈静. 基于模糊聚类水平集的医学图像分割方法[J]. 计算机科学, 2015, 42(Z11): 155-159. https://doi.org/

WU Jie, ZHU Jia-ming and CHEN Jing. Fuzzy Clustering Level Set Based Medical Image Segmentation Method[J]. Computer Science, 2015, 42(Z11): 155-159. https://doi.org/

参考文献

[1] 朱晓舒,孙权森,等.基于MultiLayer水平集的脑MRI图像分割[J].计算机科学,2014,1(9):297-300
[2] 谢强军,周泽魁,等.变分水平集理论及其在医学图像分割中的应用[D].杭州:浙江大学,2009
[3] 乔剑敏,何轶良.基于GAC模型和C-V模型的图像分割方法的改进[D].哈尔滨:哈尔滨工业大学,2011
[4] 何瑞英.含边缘信息的C-V模型[J].计算机工程与应用,2012,48(18):181-186
[5] Li Chun-ming,Xu Chen-yang,Gui Chang-feng,et al.Level Set Evolution Without Re-initialization[C]∥A New Variational Formulation Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’05) .IEEE ,2005
[6] Chan T,Vese L.Active contours without edges[J].IEEETrans.Imag.Proc.,2001,10:266-277
[7] Li Chun-ming,Xu Chen-yang,Member S, et al.IEEE Distance Regularized Level Set Evolution and Its Application to Image Segmentation[J].IEEE Transactions on Image Processing,2010, 19(12):3243-3254
[8] 曹瑜旦,罗斌.基于模糊集与水平集的医学图像自动算法研究[D].合肥:安徽大学,2014
[9] 李丙春,田军,钟志明.结合梯度与区域信息的变分水平集图像分割方法[J].新疆大学学报(自然科学版),2010,7(4):486-489
[10] 王梅芳,范红,Wang Yi .利用改进CV模型连续水平集算法的核磁共振乳腺图像分割[J].西安交通大学学报,2014,8(2):38-43
[11] 王梁,吴斌,方艳红.基于区域生长和水平集的肝脏提取分割算法[J].科学技术与工程,2014,4(3):216-221
[12] 彭建喜.一种基于C均值的模糊核聚类图像分割算法[J].电视技术,2014,8(9):28-31
[13] 陈启东,程宜康,石澄贤.利用统计概率密度的活动轮廓模型分割图像[J].中国农机学报,2014,5(1):112-117
[14] 刘建伟,郭雷.直方图的脑图像分割策略[J].西北工业大学学报,2014,4(3):188-192
[15] 王顺风,阮晶,王宇.基于改进的LBF模型的图像分割[J].计算机应用与软件,2011,8(2):25-27
[16] 武园,葛玉容.一种基于脑部肿瘤MR图像的分割算法[J].电子设计工程,2014,2(10):50-52
[17] Bezdek J C.Pattern Recognition with Fuzzy Objective Function Algorithm[M].New York:Springer-Verlag,1981

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