基于中值的平方距离对称共生矩阵阈值化方法

Median-based Square Distance Symmetrical Co-occurrence Matrix Thresholding Method

对灰度概率分布呈现偏斜和重尾的一类图像的阈值选取问题进行了研究.鉴于应用均值方法进行分类估计出现偏差的问题,本文应用中值方法进行修改,使图像阈值的选取更加合理.基于平方距离的对称共生矩阵阈值方法,在对称共生矩阵上定义了区域中值,提出了基于中值进行分类统计的平方距离对称共生矩阵阈值法,并给出了多阈值分割计算式.与Otsu′s法、基于平方距离法的分割比较表明:本文提出的方法不仅对于分类概率呈现偏斜和重尾的情况分割效果突出,而且由于考虑了图像的空间信息,与基于中值的Otsu′s法相比,所提取的目标信息更加完整,边缘更加清晰;对于小目标类的图像,该方法也具有良好的阈值选取效果.为进一步说明该方法的正确性和有效性,基于标准分割图像进行了误分类误差计算,结果表明所提出的方法误差值能够达到最小.

The image threshold selection of skew and heavy-tailed class-conditional distributions were studied. Due to the deviation of the mean-based method in classification estimation, the median-based method is more reasonable in threshold selection. Based on the square distance symmetrical co-occurrence matrix, the region median was defined, and then using median classified statistics method, a new threshold approach was proposed based on the square distance symmetrical co-occurrence matrix, and the multi-threshold segmentation algorithms was advanced. Compared with OtsuPs and square distance, the proposed method not only has prominent segmentation performance for the images of skew and heavy- tailed class-conditional distributions, hut it takes the more spatial statistical information on account, compared with median-based Otsu＇s thresholding, the extracted object information is more complete, and the edge is clearer. For the small object probability distribution images, this method also has better threshold segmentation effect. To illustrate the correctness and effectiveness, based on the ground-truth images, the misclassification error results show that the proposed method can obtain the minimum value.