摘要：

基于区域的局部二值拟合模型在处理灰度不均匀图像方面有较大优势,但其只考虑原始图像灰度的平均统计信息,对于包含大量噪声的图像通常很难获得理想的效果。为克服上述缺陷,提出一种基于原始图像和差分图像统计信息的分割模型。该模型在原始图像灰度统计信息的基础上,加入差分图像信息,分别对原始图像和差分图像构造以高斯函数为核函数的能量方程,并运用梯度下降法求解,驱使活动轮廓向目标边缘演化。实验结果表明, 与传统活动轮廓模型相比,该模型能正确提取含有噪声和信噪比低的图像,同时对初始轮廓曲线有更高的鲁棒性。

关键词: 图像分割, 活动轮廓, C-V 模型, 局部二值拟合模型, 局部高斯分布拟合模型

Abstract:

Local Binary Fitting(LBF) model based on region has great advantage in dealing with the segmentation of images with intensity inhomogeneity. Because it only considers original image statistical information,it cannot efficiently segment images with heavy noise. In order to overcome these problems,this paper proposes a novel local active contour model based on the original image and the difference image. This model that combines the difference image information is on the basis of the original image intensity statistics. The energy function is then constructed with Gaussian function as the kernel function. The contour can be driven to the objective boundaries by using the gradient descent method. Experimental results show that the proposed model is able to deal the image with noise and low signal-to-noise ratio,and can also reduce the sensitivity on the initialization of active contour when compared with the classical active contour model.

Key words: image segmentation, active contour, C-V model, Local Binary Fitting ( LBF ) model, Local Gaussian Distribution Fitting(LGDF) model

中图分类号: 

• TP301.6