中文摘要：

为提高图像分割的精度获取边缘更佳的分割图,提出结合无关曲率方向的边缘函数与无需重新初始化符号距离函数的基于C-V（Chan-Vese）模型的快速分割算法。针对在图像的同质区域中基于水平集的C-V模型不能正确分割出目标轮廓的缺陷提出优化方法。改进算法不依赖于水平集梯度信息进行活动轮廓曲线的演变,引入无关曲率的边缘函数并结合平均曲率运动方程以最小化长度能量项;并且在能量函数中增加了内能泛函项,以简化模型在局部需要重新初始化符号函数的步骤,提高运算速度。实验表明新算法能够演化出目标边缘曲线,准确分割图像,且运行耗时显著减少,收敛速度近似为几何活动轮廓C-V模型的1.2倍。

英文摘要：

To improve image segmentation accuracy with better edge details, a new fast method is proposed based on the Chan-Vese （C-V） model. It combines an edge function and a signed distance function. The edge function is directionally curvature-independent, and the energy function evolves without re-initializing the signed distance function. The improved method extends the C-V model, so as to properly extract contours from given images in homogeneous areas. It does not use the local gradient information of level sets while evolving contours, instead it adds a curvature-independent directional edge function and uses mean curvature motion to minimize length energy. The internal energy function term of the energy function is increased to simplify and speed up the model when it needs to re-initialize the signed distance function. Experiments show that the new algorithm nicely evolves wanted target edge contours for accurate image segmentation, and also reduces time significantly, approximately 1.2 times faster than the geometric active contour C-V model.