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文乔农, 刘增力, 万遂人, 徐双. 超声图像分割的曲线长度约束的图模型方法[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 545-552.
引用本文: 文乔农, 刘增力, 万遂人, 徐双. 超声图像分割的曲线长度约束的图模型方法[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 545-552.
Wen Qiaonong, Liu Zengli, Wan Suiren, Xu Shuang. Ultrasound Image Segmentation Based on Graph Model of Curve Length Constraint[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(4): 545-552.
Citation: Wen Qiaonong, Liu Zengli, Wan Suiren, Xu Shuang. Ultrasound Image Segmentation Based on Graph Model of Curve Length Constraint[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(4): 545-552.

超声图像分割的曲线长度约束的图模型方法

Ultrasound Image Segmentation Based on Graph Model of Curve Length Constraint

  • 摘要: 针对超声图像分辨率低、组织和器官间的对比度低,图像具有弱边界并且包含严重的斑点噪声,导致分割非常困难的问题,结合超声图像自身的特点,提出一种适合超声图像的交互式图割的分割方法.首先用各项异性的扩散方法去除斑点噪声;然后构造能量泛函,在能量模型中增加曲线长度的约束,建立源点、汇点和图像的像素点之间的关联,定义泛函模型的数据项和规整化项;最后给出能量泛函能用图割最小化的证明,详细推导了能量泛函模型的求解,根据能量模型的特点构建相应的s-t图,并给出了图割方法步骤.实验结果表明,该方法能够精确、快速地分割出图像中的目标,解决了图割模型容易出现小区域和曲线不光滑的问题,分割效果优于传统的图模型.

     

    Abstract: Ultrasound image segmentation is very difficult problem because of low resolution, low contrast between tissues and organs, weak borders and serious speckle noise.This paper proposes a graph cut based interactive image segmentation method according to the character of ultrasound images.First, remove speckle noise using anisotropic diffusion method;Then construct the energy functional, increase curve length constraint in energy model, creates an association between source point, sink point and image pixels, and defines data items and regularization term of functional model; Finally gives the proof that energy functional can use graph cuts to minimize, derive solution of the energy functional model in detail, construct the corresponding s-t diagram based on energy model characteristics and give steps of graph cut method.Experimental results show that this method can accurately and quickly segment the target in the image and solve small areas and no smooth problems. The segmentation effect is better than that of traditional graph model.

     

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