摘要： 针对图像分割中的灰度不均匀和轮廓初始化问题,提出一种基于区域的活动轮廓模型。将图像的全局信息和局部信息作为能量项驱动活动轮廓向目标边缘演化,以有效分割灰度不均匀图像,为保证图像分割的速度和精度,在能量方程中加入长度项和惩罚项,并采用梯度下降法得到该模型的最小化能量方程。实验结果表明,和局部二值拟合模型、局部图像拟合模型相比,该模型能分割灰度不均匀的图像,对初始轮廓曲线大小和位置更不敏感,且分割图像所需的迭代次数、迭代时间更少。

关键词: 图像分割, 活动轮廓, 水平集, C-V 模型, 局部二值拟合模型, 局部图像拟合模型

Abstract: In order to overcome the problem of weak boundary and intensity inhomogeneity,a region-based active contour model for image segmentation is proposed in this paper. Using the global and local image information as the energy term driving evolution of active contour to the objective boundaries can effectively segment images with intensity inhomogeneity. In order to segment the image fast and accurately,the length term and penalty are incorporated into the energy equation. By adopting gradient descent method, the minimization of the energy equation can be given. Segmentation tests demonstrate that the proposed method can segment images with intensity inhomogeneity,needs less iteration and few iteration times,and is less sensitive to the location and size of the initial contour when is compared with the Local Binary Fitting(LBF) model and the Local Image Fitting(LIF) model.

Key words: image segmentation, active contour, level set, C-V model, Local Binary Fitting(LBF) model, Local Image Fitting(LIF) model

中图分类号: 

• TP391. 41