摘要： 局部二值拟合模型利用图像的局部平均灰度信息能够对强度非均匀的图像进行分割,但对于存在强噪声和对比度低的医学图像,仅用局部灰度均值不能得到正确的分割结果。针对该问题,提出一种改进的局部活动轮廓模型,在考虑图像局部均值信息的同时,加入图像的全局约束信息检测轮廓线外具有较大梯度幅值的像素点,采用水平集方法最小化能量泛函,使得演化曲线能够准确地停止在目标边界的位置上。实验结果表明,改进方法提高了对噪声的鲁棒性,且其分割精度较高。

关键词: 局部二值拟合模型, 图像分割, 灰度不均匀, 局部灰度, 全局约束, 活动轮廓模型

Abstract: The Local Binary Fitting(LBF) model uses the local average gray level information of image to cope with gray level inhomogeneity.However,by only using the local gray means of the image,it is not possible to get ideal results of segmentation for the medical images which involve strong noise and low contrast.This paper puts forward an improved method.Taking the local average information into consideration,it uses the global constraint information to detect the large gradient amplitude at the outer region.By adopting the level set methods to minimize the energy function,the contour is able to stay at the objective boundaries accurately.Experimental results show that the improved method enhances the robustness to noise,and its accuracy of segmentation exceeds that of other similar methods.

Key words: Local Binary Fitting(LBF) model, image segmentation, gray level inhomogeneity, local gray, global constraint, active contour model

中图分类号: 

• TP391