摘要
针对传统C-V模型演化速度慢和不能很好分割灰度不均匀图像的缺点,从两个方面进行了改进。首先采用一个新颖的基于局部梯度的模型,使C-V模型初始轮廓曲线快速移到目标边界附近,大大缩短了演化时间;其次,结合GVF模型从两个方向指向目标边界的特点,为C-V模型的速度方程添加一个自适应速度调节项,使模型收敛于真实边界。通过肝脏肿瘤CT图像的分割,验证该方法是有效的。
Aiming at the shortcomings of slow convergence and inaccuracy segmentation in non-homogeneous images,improvements were made on the traditional C-V model in two aspects.Firstly,using a novel model based on local gradient,the initial contour of the C-V model was quickly moved near the target border,greatly reducing the evolution time.Secondly,combining the characteristics of GVF model from two directions to the target border,an adaptive velocity reconciling item was added for velocity equation of the C-V model to make the model converge to the true border.The segmentation experiments for liver tumors in CT showed that the proposed method could be effective.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2012年第2期341-346,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(50775060)
山西省教育厅项目资助(20103098)
