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| 灰度不均的弱边缘血管影像的水平集分割方法 |
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薛维琴1,2, 周志勇1,2, 张涛1,3, 李莉华4, 郑健3
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1.中国科学院 长春光学精密机械与物理研究所,吉林 长春 130033;2.中国科学院 研究生院,北京 100049;3.中国科学院 苏州生物医学工程技术研究所,江苏 苏州 215163;4.重庆电子工程职业学院,重庆 401331
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| 摘要: |
| 针对血管影像中灰度不均和弱边缘情况下已有水平集模型不能正确分割血管问题,提出一种耦合了血管影像的几何信息、边缘信息和区域信息的水平集分割方法.首先,采用Hessian 矩阵的各向异性性对血管状目标进行识别,对原始影像数据进行多尺度滤波;然后采用拉普拉斯算子零交叉点的快速边缘积分方法将边缘信息嵌入能量泛函中,构建一种基于结构、边缘和区域信息的水平集分割方法.相比于单一依靠影像边缘信息或区域信息模型及其改进模型,该方法在分割严重灰度不均匀的血管造影影像上能够准确提取血管,并精确定位血管边缘. |
| 关键词: 血管分割 灰度不均 弱边缘 水平集 边缘积分 几何结构 各向异性 管状滤波器 |
| DOI：10.3724/SP.J.1001.2012.04095 |
| 分类号: |
| 基金项目:国家自然科学基金项目(81000651); 江苏省自然科学基金项目(BK2010236); 江苏省基础研究计划(BK2011331);中国科学院知识创新工程重要方向项目(KGCX-YW-909-1); 苏州市技术专项(ZXS201003) |
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| Vessel Segmentation Under Non-Uniform Illumination: A Level Set Approach |
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XUE Wei-Qin1,2, ZHOU Zhi-Yong1,2, ZHANG Tao1,3, LI Li-Hua4, ZHENG Jian3
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1.Changchun Institute of Optics, Fine Mechanics and Physics, The Chinese Academy of Sciences, Changchun 130033, China;2.Graduate University, The Chinese Academy of Sciences, Bejing 100049, China;3.Suzhou Institute of Biomedical Engineering and Technology, The Chinese Academy of Sciences, Suzhou 215163, China;4.ChongQing College of Electronic Engineering, Chongqing 401331, China
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| Abstract: |
| In this paper, a new level set segementation model is proposed and is coupled with the geometric information, the edge information and the region information. The new level set segementation model is aimed at a vessel segmentation in a non-uniform image with weak object boundaries. First, a multiscaled filter with a Hessian matrix, which has a anisotropic character, is used to identify the direction of vessels. Second, the edge information is embed into a energy functional by a fast edge integral method with a laplacian zero crossing algorithm. A new level set segmentation model based on information of geometric structure, edge and region is constructed by this method. This new model can segment vessels exactly on grayscale uneven images. Compared to GAC CV segmentation model and other improved models based on CV model, the method in this paper has a better accuracy and robustness. |
| Key words: vessel segmentation intensity inhomogeneity weak edges level set edge integration geometrical structure anisotropic tubular filter |
