期刊文献+

融合模糊全局和双核局部信息的活动轮廓模型 被引量:2

Active Contour Model of Combining Fuzzy Global and Dual-core Local Information
下载PDF
导出
摘要 尺度可控的局部区域(RSF)活动轮廓模型可用于灰度不均匀图像的分割,但存在初始化敏感和易陷入局部极小值的缺点,从而限制了其实际应用,因此提出了一种结合模糊聚类区域信息的变分水平集活动轮廓模型.该模型采用了模糊均值聚类(FCM)算法对图像进行预处理,将预处理结果二值化后作为下一步水平集演化的初始轮廓,解决了初始化敏感问题;设计了一个灰度域上的核函数,将其与RSF模型的空域核的一个线性组合作为局部能量项,弥补了采样权值仅与空间距离有关的缺陷,提高了分割精度;将聚类分析得到的模糊隶属度作为图像的全局信息,结合改进的CV模型,作为全局拟合力,增加了模型的自适应性,并加快了模型的收敛速度.实验结果表明,该模型能够自动初始化,抗噪性能强,对灰度不均匀图像有很好的分割效果. Abstract:The Region-Scalable Fitting (RSF) model can be used to segment images with intensity inhomogeneity, but it is so sensitive to the location of initial curve and is easy to fall into local minimums that limits its practical application. Therefore, a region-based ac- tive contour model combining the fuzzy means clustering (FCM) in a variational level set formulation is proposed in this paper. In the proposed method, it uses the fuzzy means clustering algorithm for image preprocessing, and the results of binarization of the pre- processing serves as the initial contour of the Next level set evolution, which solves the sensitivity to the location of initial curve; The local energy item is defined as a linear combination of the RSF model and our model by taking domain and range kernel functions into account, which can make up for the defects that sampling weights are only related to spatial distance and improve the accuracy of seg- mentation. Also, it establishes a global fitting force by introducing the fuzzy membership of the Cluster analysis that serves as the global information of image and combine with the improved CV active contour model, which can increase the self-adaptability of the model and can also accelerate the speed of convergence of the proposed model. Experimental results show that the proposed model al- lows for automatic initialization and is less sensitive to noise, while it has been applied to images with intensity inhomogeneity with desirable results.
出处 《小型微型计算机系统》 CSCD 北大核心 2014年第3期663-666,共4页 Journal of Chinese Computer Systems
基金 河北省卫生厅科研基金项目(20120395)资助
关键词 图像分割 活动轮廓模型 模糊均值聚类 水平集函数 灰度不均 初始轮廓线 image segmentation active contour model fuzzy means clustering level set function intensity inhomogeneity initial contour
  • 相关文献

参考文献3

二级参考文献37

  • 1周则明,陈强,王平安,夏德深.结合模糊C均值聚类和曲线演化的心脏MRI图像分割[J].系统仿真学报,2005,17(1):129-133. 被引量:12
  • 2闵莉,李小毛,唐延东.一种改进的无需水平集重新初始化的C-V主动轮廓模型(英文)[J].光电工程,2006,33(9):52-58. 被引量:12
  • 3唐利明,何传江,申小娜.几何活动轮廓模型的多尺度扩散分割算法[J].计算机辅助设计与图形学学报,2007,19(5):661-666. 被引量:9
  • 4Chan T, Vese L. Active contours without edges [J]. IEEE Transaction on Image Processing, 2001, 10(2): 266-277. 被引量:1
  • 5WANG Wen-yu. An Active Contour Model For Selective Segmentation [C]// International Conference on Computer Graphics, Imaging and Vision: New Trends. Washington, DC, USA: IEEE Press, 2005: 259-260. 被引量:1
  • 6Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: a new variational formulation [C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE Press, 2005, 1: 430-436. 被引量:1
  • 7Li Chnnming, Kao Chiu-Yen, Gore J C, et al. Implicit Active Contours Driven by Local Binary Fitting Energy [C]//IEEE Conferenee on Computer Vision and Pattern Recognition (CVPR), Minnesota, USA: IEEE Press, 2007: 1-7. 被引量:1
  • 8WANG Yuping, DANG Chuangyin. An Evolutionary Algorithm for Global Optimization Based on Level-Set Evolution and Latin- Squares [J]. IEEE Transactions on Evolutionary Computation, 2007, 11(5): 579-595. 被引量:1
  • 9Caselles V, Kimmel R, Sapiro G. Geodesic active contours [J]. Int'l J. Comp. Vis, 1997, 22: 61-79. 被引量:1
  • 10LIU Huafeng, CHEN Yunmei, CHEN Wufan. Neighborhood aided implicit active contours [C]// IEEE Conference on Computer Vision and Pattern Recognotion (CVPR). New York, USA: IEEE Press, 2006, 1: 841-848. 被引量:1

共引文献68

同被引文献8

引证文献2

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部