Image segmentation is a popular technique that is used for extracting information from images, which has also gained a lot of interest lately due to its importance in different scientific fields such as the medical field. This paper proposes a novel image segmentation technique using Expectation-Maximization (EM) clustering algorithm and Grasshopper Optimizer Algorithm (GOA). The proposed technique and the concept of image segmentation are effectively applied on dental radiography datasets that are collected from 120 patients with an age between 6 to 60 years old. To validate the proposed technique, a comparison in terms of purity and entropy measures is conducted against K-means, X-means, EM, and Farthest First algorithms. Based on our experimental results, the proposed technique using EM and GOA achieved the best results compared to other algorithms for all three datasets in terms of entropy and purity. The best results were obtained using the second dataset, which achieved purity value of 0.7126 and entropy value of 0.3083. Further, the proposed technique also outperforms U-net and Random Forest algorithms for the selected datasets.

图像分割是一种用于从图像中提取信息的流行技术，由于其在医学领域等不同科学领域中的重要性，近来也引起了很多兴趣。本文提出了一种基于期望最大化（EM）聚类算法和蚱hopper优化算法（GOA）的图像分割技术。所提出的技术和图像分割的概念有效地应用于从120例年龄在6至60岁之间的患者收集的放射线照相数据集中。为了验证所提出的技术，在纯度和熵测度方面与K均值，X均值，EM和Farthest First算法进行了比较。根据我们的实验结果，与所有其他三个数据集的算法相比，在熵和纯度方面，使用EM和GOA的拟议技术均获得了最佳结果。使用第二个数据集可获得最佳结果，其纯度值为0.7126，熵值为0.3083。此外，对于选定的数据集，拟议的技术还优于U-net和随机森林算法。