基于Sugeno补的广义模糊熵阈值分割方法

A Generalized Fuzzy Entropy Thresholding Segmentation Method Based on the Sugeno Complement Operator

鉴于传统的基于模糊熵的图像阈值分割方法对于光照不均匀图像的分割结果很不理想,该文提出了基于Sugeno补的广义模糊熵图像阈值分割方法。首先按照Sugeno补函数不动点的变化,对一幅图像产生9个阈值,然后利用图像分割质量评价指标对这9个阈值进行评价,最后选择使得评价指标最大的阈值作为最优的阈值。与传统的模糊熵阈值分割方法相比,新方法增加了选择更好的分割结果的机会,对于光照不均匀的图像能够获得比传统模糊熵方法更好的分割效果。

For images with bad illumination, the traditional fuzzy entropy thresholding segmentation method can not achieve satisfactory results. In this paper a generalized fuzzy entropy thresholding method based on the Sugeno complement function is presented. Firstly, nine thresholds are obtained for an image based on the variations of the fixed point in the Sugeno complement function. Secondly, the nine thresholds are evaluated by an image segmentation quality evaluation principle. Finally, the threshold with the maximum quality evaluation value among the nine thresholds is chosen as the optimal threshold. Compared with the traditional fuzzy entropy method new method increases the opportunity of choosing an optimal threshold and obtains better segmentation result for images with bad illumination.