一种结合二维熵和模糊熵的图像分割方法

基于二维熵的分割方法是一种常用的阈值分割技术,其基本假设是对象区域和背景区域占据了二维直方图的绝大部分区域,即假设对象区域和背景区域的概率和近似为1。该方法存在的不足是忽略了边界区域的信息对分割结果的影响,鉴于此,提出了一种结合二维熵和模糊熵的图像分割方法,先采用二维熵对图像进行初步分割,再采用模糊熵作后续处理以弥补忽略边界信息带来的问题。实验结果表明,对于含噪图像,该文方法的分割效果是比较理想的。

One Image Segmentation Method Combining 2-D Entropic Method and Fuzzy Entropy

2-D entropic thresholding method is a common image segmentation method.A basic assumption of this method is that regions of object and background cover almost all of the 2-D histogram,that is,the probability sum for object region and background region is close to 1.This assumption leads to neglecting the effect of edges information.In the light of this,an image segmentation method combining 2-D entropic and fuzzy entropy is proposed.Firstly,pre-segmentation is made by 2-D entropic method,secondly,further processing using fuzzy entropy is done in order to offset neglecting the information of edges.Experimental results show that this method can obtain better post-process for images with noise.