计算机科学 ›› 2016, Vol. 43 ›› Issue (Z11): 193-196.doi: 10.11896/j.issn.1002-137X.2016.11A.043

• 模式识别与图像处理 • 上一篇    下一篇

结合分水岭算法的水平集医学图像分割方法

张辉,朱家明,陈静,吴杰   

  1. 扬州大学信息工程学院 扬州225127,扬州大学信息工程学院 扬州225127,扬州大学信息工程学院 扬州225127,扬州大学信息工程学院 扬州225127
  • 出版日期:2018-12-01 发布日期:2018-12-01

Level Set Medical Image Segmentation Method Combining Watershed Algorithm

ZHANG Hui, ZHU Jia-ming, CHEN Jing and WU Jie   

  • Online:2018-12-01 Published:2018-12-01

摘要: 由于医学图像中的复杂目标通常难以被完全分割,提出标记分水岭与改进型Li模型的组合图像分割算法。改进型Li模型构造了符号压力函数来取代传统的停止函数,解决了曲线单向演化的问题。标记分水岭具有较强的抑制噪声的能力,对医学图像的弱边缘具有较强的捕获能力。所以首先运用标记分水岭算法对图像进行预分割,快速准确定位目标区域边缘信息。再引入改进型Li模型算法,通过符号压力函数来指引曲线演化方向,控制演化速度大小,实现对复杂目标的完全分割。实验结果表明:全局信息和边缘信息都能被获得,该组合算法对医学图像中的复杂目标的分割效果较满意。

关键词: 医学图像分割,标记分水岭,改进型Li模型,符号压力函数

Abstract: Complex targets of medical image are usually difficult to be completely segmented,so an image segmentation algorithm of modified Li model combining mark watershed algorithm was proposed.A symbol pressure function replaces the traditional stop function in Modified Li model,and the problem of unidirectional curve evolution is solved.Mark watershed has both stronger ability to suppress noise and stronger ability to capture weak edge of medical image.Firstly,mark watershed algorithm is used for image segmentation pretreatment,positioning information of target edge fast and accurately.Then,the modified Li model algorithm is introduced,and the symbol pressure function is used to guide curve evolution direction and control the size of the evolution speed,realizing full segmentation of complex object.The experimental results show that global information and edge information can be gooten,and the combination algorithm of complex targets can get satisfactory effect in the medical image segmentation.

Key words: Medical image segmentation,Mark watershed,Modified Li model,Sign pressure function

[1] 江贵平,秦文健,周寿军,等.医学图像分割及其发展现状[J].计算机学报,2015,8(6):1222-1241
[2] 刁智华,赵春江,郭新宇,等.分水岭算法的改进方法研究[J].计算机工程,2010,6(17):4-6
[3] Osher S,Sethian J A.Fronts propagating with curvature dependent speed:Algorithms based on the Hamilton-Jacobi formulation [J].Journal of Computational Physics,1998,79:12-49
[4] Li C M,Kao C Y,Gore J C,et al.Minimization of region-scalable fitting energy for image segmentation [J].IEEE Trans on pattern Analysis and Machine Intelligence,2008,7(10):1940-1949
[5] Li C,Xu C,Gui C,et al.Level set evolution without re-initialization:A new variational formulation[C]∥IEEE Conference on Computer Vision and Patten Recognition(CVPR).2005:430-436
[6] Vincent L,Soilh P.Watersheds in digital space:An efficient algorithm based on immersion simulations [J].IEEE Translations on Pattern Analysis and Machine Interpretation,1991,13(6):583-598
[7] 阮秋琦,阮宇智,等.数字图像处理[M].北京:电子工业出版社,2011:497-502
[8] 赵珊,王水.结合进化规划的图像分水岭分割技术[J].计算机科学,2011,38(5):265-267
[9] 方江雄.变分和偏微分方法在图像分割中的应用[M].北京:中国石化出版社,2015:20-23
[10] Xu C Y,Yezzi A,Prince J L.On the relationship between parametric and geometric active contour[C]∥Processing of 34th Asilomar Conference on Signals Systems and Computer.Pacific Grove:IEEE Press,2000:483-489
[11] Salah M B,Mitiche A,Ayed I B.Multiregion image segmentation by parametric kernel graph cuts[J].IEEE Transactions on Image Processing,2011,20(2),545-557
[12] Han X Z,Jian Z.A nonlinear image enhancement algorithmbased on partial differential equations[C]∥IEEE 10th International Conference Signal Processing.2010:1114-1116

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!