期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于PSO的Live Wire交互式图像分割算法 被引量：2

PSO-Based Live Wire Interactive Image Segmentation Algorithm
下载PDF
导出
摘要 传统Live Wire算法易受伪轮廓干扰,并且算法执行速度较慢.针对这些问题,提出一种基于PSO的Live Wire交互式图像分割算法.算法首先构造新的代价函数,引入相邻节点间梯度幅值变化函数来减轻伪轮廓的干扰,提高了算法的分割精度;其次,为了提高算法的执行效率,应用粒子群算法求取图像中任意两点间最短路径来定位目标边界,并与经典的基于Dijkstra动态规划图搜索的Live Wire算法进行比较.实验结果表明,与传统方法相比,所提算法在分割精度和执行效率上都有很大提高. A PSO-based live wire interactive image segmentation algorithm was proposed to solve the problems that the objective contour is easily influenced by imaging artifacts and imaged at low computation speed.With a new cost function formulated,the gradient amplitude change function between adjacent nodes was introduced into the new cost function to reduce the interference due to imaging artifacts and improve the segmentation accuracy.To improve the implementing efficiency of the algorithm proposed,PSO was applied to finding out the shortest path between any two points in image so as to locate the objective edge,and the relevant results were compared with the typical Live Wire image segmentation algorithm based on the search by the Dijkstra algorithm.It was found that the proposed algorithm is more accurate and efficient.
作者 依玉峰 高立群 崔兆华 史圣伦
机构地区 东北大学信息科学与工程学院 东北财经大学会计学院
出处 《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2011年第2期193-196,201,共5页 Journal of Northeastern University(Natural Science)
基金 国家自然科学基金资助项目(60674021)
关键词 LIVE Wire算法 图像分割 粒子群优化 最短路径 DIJKSTRA算法 Live Wire algorithm image segmentation PSO（particle swarm optimization） shortest path Dijkstra algorithm
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献9

  • 1Mortensen E N, Barrett W A. Intelligent scissors for image composition[ C] // Proceedings of the ACM SIGGRAPH 95: 22nd International Conference on Computer Graphics and Interactive Techniques. Los Angeles, 1995 : 191 - 198.
  • 2Mortensen E N, Barrett W A. Interactive segmentation with intelligent scissors [J].Graphical Models and Image Processing, 1998,60 (5) : 349 - 384.
  • 3Artur C, Ulf M, Langilte M, et al. Color lesion boundary detection using live wire [ C ]//Image Prccessing on Medical Imaging. Bellingham, 2005 : 1589 - 1596.
  • 4Farber M, Ehrhardt J, Handels H. Automaic atlas-based contour extraction of anatomical structures in medical images [J]. Computer Assisted Radiology and Surgery, 2005, 1281:272 - 277.
  • 5Kennedy J, Eberhart R C. Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks. New York: IEEE, 1995:1942-1948.
  • 6陈炳瑞,冯夏庭.压缩搜索空间与速度范围粒子群优化算法[J].东北大学学报（自然科学版）,2005,26(5):488-491. 被引量：20
  • 7Montes de Oca M A, Stutzle T, Birattari M, et al. Frankenstein's PSO: a composite particle swarm optimization algorithm [ J ]. IEEE Transactions on Evolutionary Computation, 2009,13(5):1120- 1132.
  • 8Mohemmed A W, Sahco N C, Geok T K. Solving shortest path problem using particle swarm optimization[J ]. Applied Soft Computing, 2008,8(4) : 1643 - 1653.
  • 9Carlisle A, Dozier G. An off-the-shelf PSO[C]//Proceedings of the Workshop on Particle Swarm Optimization. Indianapolis, 2001 : 1 -6.

二级参考文献12

  • 1Kennedy J, Eberhart R C. Particle swarm optimization[A].Proc IEEE Int Conf Neural Networks [C]. Piscataway:IEEE Press, 1995. 1942 - 1948.
  • 2Eberhart R C, Kennedy J. A new optimizer using particle swarm theory [ A ]. Proc of the Sixth International Symposium on Micro Machine and Human Science [ S ].Nagoya, 1955: 39 - 43.
  • 3Voss M S, Feng X. ARMA model selection using particle swarm optimization and AIC criteria[A]. 15th Triennial World Congress [ C]. Barcelona: IFAC, 2002.117 - 129.
  • 4van den Bergh F, Engelbrecht A P. Cooperative learning in neural networks using particle swarm optimizers [ J ]. South African Computer Journal, 2000 ( 11 ): 84 - 90.
  • 5Shi Y H, Eberhart R C. A modified particle swarm optimizer[A]. IEEE World Congress on Computational Intelligence[C]. Anchorage, 1998.69 - 73.
  • 6Eberhart R C, Shi Y H. Comparing inertia weights and constriction factors in particle swarm optimization[ A]. Proc 2000 Congress Evolutionary Computation [C]. Piscataway:IEEE Press, 2000.84 - 88.
  • 7Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimizer with breeding and subpopulations[A]. Proc of the 3rd Genetic and Evolutionary Computation Conference[C]. Sanfrancisco, 2001. 469 - 476.
  • 8Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization [ A]. Proc 1999Congress Evolutionary Computation [C]. Piscataway: IEEE Press, 1999. 1951 - 1957.
  • 9Clerc M, Kennedy J. The particle swarm explosion,stability, and convergence in a multidimensional complex space[J]. IEEE Trans Evolutionary Computation, 2002,6(1):58-73.
  • 10柯晶,钱积新.非线性离散时间系统的自适应函数观测器[J].电路与系统学报,2003,8(3):1-5. 被引量：2

共引文献19

同被引文献32

  • 1Zhang B, Sun S B, SunJ W, et al . 3D reconstruction method from biplanar radiography using DL T algorithm: application to the femur[CJ //Proceedings of the 1st International Conference on Pervasive Computing Signal Processing and Applications. Los Alamitos: IEEE Computer Society Press, 2010: 251-254.
  • 2Boykov Y y,Jolly M P. Interactive graph cuts for optimal boundary &. region segmentation of objects in N -D images[CJ //Proceedings of the 8th IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2001, 1: 105-112.
  • 3Gamage r , Xie S Q, Delmas r. et al. Segmentation of radiographic images under topological constraints: application to the femur[J]. InternationalJournal of Computer Assisted Radiology and Surgery, 2010, 5(5): 425-435.
  • 4Galibarov P E, Prendergast PJ, Lennon A B. A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs[J]. Medical Engineering &. Physics, 2010, 32(10): 1180-1188.
  • 5Wang L, Li C M, Sun Q S, et al. Brain MR image segmentation using local and global intensity fitting active contours/ surfaces[MJ / /Lecture Notes in Computer Science. Heidelberg: Springer, 2008, 5241: 384-392.
  • 6Li C M, Xu C v; Anderson A W, et al . MRI tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework[MJ / /Lecture Notes in Computer Science. Heidelberg: Springer, 2009, 5636: 288-299.
  • 7Zhang T, Freedman D. Tracking objects using density matching and shape priors[CJ //Proceedings of the 9th IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2003, 2: 1056-1062.
  • 8Kichenassamy S, Kumar A, Olver P, et al. Gradient flows and geometric active contour models[CJ IIProceedings of the 5th International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 1995: 810-815.
  • 9Chan T F, Vese L A. Active contour without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2): 266- 277.
  • 10Li C M, Kao C Y, GoreJ C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing, 2008, 17 (10): 1940-1949.

引证文献2

二级引证文献2

东北大学学报（自然科学版）
2011年 第2期

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