一种改进的二维最小交叉熵图像分割方法

Improved Image Segmentation Based on 2-D Minimum Cross Entropy

针对当前二维最小交叉熵阈值法存在计算复杂度高等问题,提出了一种改进的二维最小交叉熵阈值分割方法。首先,依据图像的含噪声类型选择邻域模板并建立相应的二维直方图来提高分割效果;然后,对二维最小交叉熵公式进行推导和简化处理,利用定义的数组运算推导出新型递推算法,再确定图像及其邻域图像的实际灰度级别范围,并用这种新算法在所求的灰度级别范围内搜索最佳阈值向量来降低计算复杂度;最后,使用关键阈值对滤波后的图像进行分割达到最佳的分割效果。仿真实验结果表明,与当前的二维最小交叉熵阈值分割法相比,本文提出的方法不仅分割性能及抗噪性能更强,而且分割时间大大减少,小于0.05s。

In view of the problems of the current thresholding method based on 2-D minimum cross entropy,such as computing complexity,an improved image segmentation method based on 2-D minimum cross entropy is presented.Firstly,a neighborhood mask was selected according to the image type including the noise and a corresponding 2-D histogram was created to improve the segmentation performance.Then,the thresholding formula of 2-D minimum cross entropy was simplified,and through the defined array operations,the recursive algorithm was combined into a new search algorithm.The gray limits of the image and the neighborhood image were obtained,and in the limits,the new recursive algorithm was used to search the best threshold vector and to reduce the computing complexity.Finally,the neighborhood image was segmented with the key threshold to obtain better segmentation effects.Experimental results show that the proposed method＇s segmentation effect and its anti-noise are better than those of the current thresholding method based on 2-D minimum cross entropy,and its computing time is much less,below 0.05 second.