The image segmentation based on Multilevel thresholding has attracted more attention in recent years, they have been used in different applications. Therefore, several evolutionary computation methods have been proposed to determine the optimal threshold values. However, these approaches suffer from some limitations such as the stagnation point which leads to degradation the quality of the segmented image. In addition, most of them used either Otsu or Kapur as a fitness function, and the complexity of these methods is increased with increasing the threshold levels. Moreover, they don’t provide accurate results in all the cases. To overcome such situations, in this paper is proposed the use of evolutionary computation algorithms combined with the Type II Fuzzy Entropy as the objective function. Such methods are the Backtracking Search Optimization Algorithm (BSA) and the Salp Swarm Algorithm (SSA). The BSA and SSA are able to avoid the limitation of similar techniques for image threshold because the objective function removes the ambiguities helping to find more accurate results. The BSA and SSA are used to find the best parameters of the Type II Fuzzy Entropy that extracts the optimal thresholds that properly segment the histogram of a digital image. To evaluate the performance of the proposed two methods, a set of experiments are performed using a set of benchmark images which have different characteristics. Moreover, the experiments are also performed over medical images from blood cells. The experimental results indicate that the proposed two methods have a good performance. However, they provide better performance than other algorithms in terms of quality and accuracy.

近年来，基于多级阈值的图像分割技术引起了越来越多的关注，并且已经在不同的应用中得到了应用。因此，已经提出了几种进化计算方法来确定最佳阈值。但是，这些方法受到一些限制，例如停滞点，这会导致分割图像的质量下降。此外，它们中的大多数都使用Otsu或Kapur作为适应度函数，并且这些方法的复杂性随着阈值水平的提高而增加。而且，它们不能在所有情况下提供准确的结果。为了克服这种情况，本文提出了结合II型模糊熵作为目标函数的演化计算算法。这样的方法是回溯搜索优化算法（BSA）和Salp Swarm算法（SSA）。BSA和SSA能够避免相似技术对图像阈值的限制，因为目标函数消除了有助于找到更准确结果的模糊性。BSA和SSA用于查找II型模糊熵的最佳参数，该参数提取可正确分割数字图像直方图的最佳阈值。为了评估所提出的两种方法的性能，使用一组具有不同特征的基准图像进行了一组实验。此外，还对来自血细胞的医学图像进行了实验。实验结果表明，所提出的两种方法具有良好的性能。然而，