一种具有去噪能力的全局最小值分割模型

几何主动轮廓模型的能量泛函是非凸性的，导致图像分割结果依赖于曲线的初始化条件，对噪声敏感。针对该问题，提出一种全局最小值分割模型，对能量泛函进行凸性非约束改进，利用基于总变分对偶公式的快速数值化算法实现图像的分割。对合成图像和医学图像的分割结果表明，利用该模型可以准确提取出对象的边界，分割速度快，对噪声具有较好的鲁棒性。

Global Minimum Segmentation Model with Ability of Denoising

Aiming at the limitations of the un-convex of energy functional in the active contour model,which makes the segmentation result sensitive to noise and the location of initial contours,a new model is proposed to make it be convex by the way of an un-constrained,and provides the fast algorithm based on the TV-norm.Experimental results on synthetic and medical images show that the proposed model can converges to a global minimum,accurately and fast segment the object,and is robust to image noise.