《计算机应用研究》|Application Research of Computers

融合全局和局部信息的水平集乳腺MR图像分割

Level set method combined global and local information and its application to breast MRI

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作者 张旭梅,范虹,乔柱
机构 1.陕西师范大学 计算机科学学院,西安 710100;2.连云港出入境检验检疫局,江苏 连云港 222042
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文章编号 1001-3695(2015)01-0307-05
DOI 10.3969/j.issn.1001-3695.2015.01.072
摘要 针对乳腺核磁共振成像的灰度不均匀现象,提出一种融合全局和局部信息的水平集图像分割方法(global and local combined C_V,GLCCV)。该方法将图像的局部信息融入基于全局信息的Chan-Vese(C_V)水平集方法;根据局部灰度拟合均值占全局灰度均值的比例,构造自适应平衡指示函数调节全局和局部效应之间的均衡;加入惩罚项以避免重新初始化。对比实验表明,该水平集分割模型能够有效分割多种灰度不均匀场景下的乳腺MR图像,在抗噪和精确性方面优于融合前的分割方法。
关键词 乳腺MRI;融合全局和局部信息;水平集;灰度不均匀;自适应指示函数
基金项目 陕西省科学技术研究发展计划项目(2012K06-36)
陕西师范大学中央高校基本科研业务费资助项目(GK201102006)
本文URL http://www.arocmag.com/article/01-2015-01-072.html
英文标题 Level set method combined global and local information and its application to breast MRI
作者英文名 ZHANG Xu-mei, FAN Hong, QIAO Zhu
机构英文名 1. School of Computer Science, Shaanxi Normal University, Xi'an 710100, China; 2. Lianyungang Entryexit Inspection & Quarantine Bureau, Lianyungang Jiangsu 222042, China
英文摘要 To effectively perform breast magnetic resonance imaging(MRI) segmentation in the case of intensity inhomogeneity, the paper proposed a novel level set method combined global and local information(GLCCV). Local information of image was incorporated into the Chan-Vese(C_V) model, which was based on global information. According to the proportion of the local intensity fitting term accounts for the global term, this paper presented a self-adaptive indicator function to balance the global and local effect, added penalty term to avoid re-initialization. Contrast experiments show that the proposed method is efficient to segment breast MRI in variety case of intensity inhomogeneity scenes, and is better than conventional methods in noise resistance and accuracy.
英文关键词 breast MRI; combined global and local information; level set method; intensity inhomogeneity; self-adaptive indicator function
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收稿日期 2013/12/19
修回日期 2014/2/3
页码 307-311
中图分类号 TP391.72
文献标志码 A