基于局部Chan-Vese模型的超声颈动脉图像水平集分割方法研究

计算机科学 ›› 2013, Vol. 40 ›› Issue (Z11): 304-308.

基于局部Chan-Vese模型的超声颈动脉图像水平集分割方法研究

曾雅洁,杨鑫,徐红卫,刘洋,梁华庚,丁明跃

华中科技大学生命科学与技术学院生物医学工程系图像信息处理与智能控制教育部重点实验室武汉430074;华中科技大学图像识别与人工智能研究所多谱信息处理技术国防科技重点实验室武汉430074;郑州大学郑州450052;郑州大学郑州450052;华中科技大学武汉430022;华中科技大学生命科学与技术学院生物医学工程系图像信息处理与智能控制教育部重点实验室武汉430074

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金国际合作项目(30911120497),国家科技支撑计划项目(2012BA113B02),国家重大科学研究计划项目(2011CB933103),国家自然科学基金青年项目(61001141),教育部新教师青年基金(20090142120091),湖北公益性科技研究项目(2012DCA06001)等资助

Research on Ultrasound Carotid Image Segmentation Methods Based on Local Chan-Vese Model Using Level Set Method

ZENG Ya-jie,YANG Xin,XU Hong-wei,LIU Yang,LIANG Hua-geng and DING Ming-yue

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 对超声主颈动脉(Common Carotid Artery,CCA)横向图像中血管的内外膜进行分割,分割结果可用于对斑块大小、厚度和形状的定性估计及定量测量。首先选用局部C-V(Local Chan-Vese,LCV)模型对外膜进行分割,而用C-V模型对内膜进行分割,并引入内外膜距离限制项来提高内膜分割准确度,同时使用稀疏场方法(Sparse Field Method,SFM)提高水平集算法的效率,最后通过全正交法(Full-Orthogonal Method,FOM)、射线法、相似系数分析法等多种评价方法对分割结果进行分析。实验结果表明,LCV模型可有效地分割颈动脉血管外膜,而C-V模型可有效地分割血管内膜,改进方法提升了程序运行速度并且提高了内外膜的分割精度。

关键词: 生物医学工程,图像分割,颈动脉超声图像,水平集,局部C-V模型

Abstract: The segmentation of intima and adventitia of Common Carotid Artery(CCA) in ultrasound transverse images is critical,and the results can be used for qualitative estimates and quantitative measurements of plaque size,thickness and shape．Firstly the adventitia was segmented using Local C-V model and the intima was segmented using C-V model．Distance limitations item was proposed to limit the evolution of the intima,and Sparse field method(SFM) was used to improve efficiency of the level set method．The result was analyzed and compared by full-orthogonal method (FOM),ray method and Dice index．The results indicated that the LCV model can effectively segment the adventitia of the caro-tid artery；C-V model can effectively segment the intima；Improved methods can increase the speed of the program and improve the accuracy of segmentation of the intima and adventitia．

Key words: Biomedical engineering,Image segmentation；Carotid ultrasound images,Level set,Local C-V model

曾雅洁,杨鑫,徐红卫,刘洋,梁华庚,丁明跃. 基于局部Chan-Vese模型的超声颈动脉图像水平集分割方法研究[J]. 计算机科学, 2013, 40(Z11): 304-308. https://doi.org/

ZENG Ya-jie,YANG Xin,XU Hong-wei,LIU Yang,LIANG Hua-geng and DING Ming-yue. Research on Ultrasound Carotid Image Segmentation Methods Based on Local Chan-Vese Model Using Level Set Method[J]. Computer Science, 2013, 40(Z11): 304-308. https://doi.org/

参考文献

[1] 金娇英,王龙会,丁明跃．两种超声颈动脉血管斑块图像分割方法比较与改进[J]．计算机科学,2012,B06:485-488
[2] 张丹丹,董小黎,杨黎明．不稳定动脉粥样硬化斑块的研究进展[J]．现代生物医学进展,2012,5:4961-4963
[3] Pock T,Cremers D,Bischof H．An algorithm for minimizing the Mumford-Shah functional[C]∥IEEE 12th International Confe-rence on Computer Vision．Kyoto,Japan,2009:1133-1140
[4] 张文娟,冯象初．基于区域的图切割算法求解Mumford-Shah图像分割模型[J]．计算机科学,2012,2:297-301
[5] Chan T F,Vese L．Active contours without edges [J]．IEEE Transactions on Image Processing,2001,10(2):266-277
[6] 杨勇,马志明,徐春． LCV模型在医学图像分割中的应用[J]．计算机学报,2010,36(10):184-186
[7] He Lin,Osher S．Solving the Chan-Vese Model by a Multiphase Level Set Algorithm Based on the Topological Derivative[J].Lecture Notes in Computer Science,2007,5:777-788
[8] 张志强,王万玉．一种改进的双边滤波算法 [J].中国图象图形学报A,2009,14(3):443-447
[9] Wang Xiao-Feng,Huang De-Shuang,Xu Huan．An efficient local Chan-Vese model for image segmentation[J]．Pattern Recognition,2010,3:603-618
[10] Kirsten K,Loya P．Calculation of determinants using contour integrals[J]．American Journal of Physics,2008,76(1):60-64
[11] Sreedhar K,Panlal B．Enhancement of Images using Morpholo-gical Transformation [J]．International Journal of Computer Science & Information Technology,2012,4(1):33-50
[12] Zorn K K．Advancing our understanding of endometrial cancer histologic subtypes:Should we lump or split[J]．Gynecologic Oncology,2007,6(1):6-7
[13] Adalstein D,Sethian J．A fast level set method for propagating interfaces[J]．Journal of Computational Physics,1994,118(2):269-277
[14] Whitaker R．A level-set approach to 3D reconstruction fromrange data[J].International Journal of Computer,1998,29(3):203-231
[15] Cheong L-F,Xiang Xu．Behaviour of SFM algorithms with erroneous calibration [J]．Computer Vision and Image Understan-ding,2011,5:16-30
[16] 刘民士,王春．射线法判断点与多边形内外关系的改进算法[J]．滁州学院学报,2010,12(2):14-16
[17] 田考聪．描述性统计分册[M]．北京:人民卫生出版社,2004:108-110

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed