基于Otsu准则及图像熵的阈值分割算法

Threshold Selection Algorithm for Image Segmentation Based on Otsu Rule and Image Entropy

在图像分割中,阈值的选取至关重要,在经典的Otsu准则基础上,结合图像熵提出了一种改进的局部递归的阈值选取及分割算法。基于图像像素熵信息,运用递归思想局部搜索图像的最佳阈值,这样不但缩短了计算时间,而且具有较好的自适应特点。该算法在图像背景不均匀或图像不是简单的单峰、双峰图像的情况下可以进行有效的分割,分割后的图像细节更加丰富,有利于分割后的特征提取。对Lena图像进行了实验,获得了较好的分割结果。

In image segmentation, threshold selection is very important. A partial recursive algorithm of threshold selection and segmentation is put forward, which is based on the Otsu threshold selecting method. Based on the information of entropy of image pixels, a partial recursive algorithm is used to search optical threshold. It not only reduces the running time, but also has better self-adaptability. With this algorithm, the image can be segmented effectively even if it is uneven and not the single-modal or bimodal one. The segmentation result has more details, which is good to the feature extraction. An experiment with Lena image is made and good result is obtained.