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自适应局部区域型水平集分割算法
刘帅1,2,
夏莉1,3,
周燕飞1,3,
刘苓苓1,3,
沈柱4,
李海1,3
(1.中国科学院 合肥物质科学研究院 医学物理与技术中心 医学物理与技术安徽省重点实验室, 合肥 230031;2.中国科学技术大学, 合肥 230026;3.中国科学院 合肥肿瘤医院, 合肥 230031;4.安徽医科大学 第四附属医院影像科, 合肥 230000)
Adaptive Level Set Segmentation Algorithm Based on Local Region
LIU Shuai1,2,
XIA Li1,3,
ZHOU Yan-Fei1,3,
LIU Ling-Ling1,3,
SHEN Zhu4,
LI Hai1,3
(1.Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;2.University of Science and Technology of China, Hefei 230026, China;3.Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China;4.Department of Imaging, the 4 th Affiliated Hospital of Anhui Medical University, Hefei 230000, China)
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Received:February 17, 2017 Revised:March 06, 2017
中文摘要: 灰度不均匀现象普遍存在于自然图像和医学图像中，因此使用传统的图像分割方法很难精准的分割出目标物，从而导致图像分割在模式识别和临床医学的应用中会出现很多问题.为了更好地改善分割效果，解决灰度不均匀现象所带来的问题，本文结合图像的自适应梯度权重信息和局部区域信息提出一种新型的水平集分割算法.由于图像的梯度信息具有稳定性，因此文中通过在局部区域中使用自适应梯度权重信息，达到结合图像边缘信息和区域信息的目的以提高算法鲁棒性.同时，文中使用的梯度权重滤波增加了图像对比度，因此分割的效果有了显著改善.最后，通过与LCV （local Chan-Vese）模型和LIC （local intensity clustering）模型的对比实验来验证本文分割方法的有效性和鲁棒性.在实验对比中，本文方法均得到比较令人满意的结果，充分展示其在处理灰度不均匀图像上的优势.
Abstract:Intensity inhomogeneity often occurs in natural and medical images, and it is hard to accurately segment intensity inhomogeneous images because most popular segmentation models are based on intensity homogeneous images. In this paper, we propose a novel level set-based segmentation model which integrates adaptive gradient weighted information (AGWI) and local region information to handle intensity inhomogeneous images. By employing AGWI in local regions, we combine the edge information and region information. Furthermore, the complementation of edge information and region information will enhance the robustness and effectiveness of our method. Finally, we compare our model with the local Chan-Vese (LCV) model and local intensity clustering (LIC) model. Some experiments on synthetic and nature images will be shown to demonstrate the efficiency and robustness of our method.
文章编号： 中图分类号： 文献标志码：
基金项目:安徽省科技重大专项（15czz02024）；国家自然科学基金（81401483）
| Author Name | Affiliation |
| LIU Shuai | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
University of Science and Technology of China, Hefei 230026, China |
| XIA Li | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| ZHOU Yan-Fei | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| LIU Ling-Ling | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| SHEN Zhu | Department of Imaging, the 4 th Affiliated Hospital of Anhui Medical University, Hefei 230000, China |
| LI Hai | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| Author Name | Affiliation |
| LIU Shuai | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
University of Science and Technology of China, Hefei 230026, China |
| XIA Li | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| ZHOU Yan-Fei | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| LIU Ling-Ling | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
| SHEN Zhu | Department of Imaging, the 4 th Affiliated Hospital of Anhui Medical University, Hefei 230000, China |
| LI Hai | Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China |
引用文本：
刘帅,夏莉,周燕飞,刘苓苓,沈柱,李海.自适应局部区域型水平集分割算法.计算机系统应用,2017,26(11):145-151
LIU Shuai,XIA Li,ZHOU Yan-Fei,LIU Ling-Ling,SHEN Zhu,LI Hai.Adaptive Level Set Segmentation Algorithm Based on Local Region.COMPUTER SYSTEMS APPLICATIONS,2017,26(11):145-151
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