Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

中文翻译：

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基于布谷鸟搜索和广义高斯的图像分割替代阈值技术

对象分割是数字图像处理中一个广泛研究的课题，因为它可以用于多个领域的无数应用。这个过程传统上是通过从图像强度直方图计算最佳阈值来实现的。已经提出了几种算法来根据不同的统计原理找到这个阈值。然而，由于可能干扰图像的许多变量，通过这些算法生成的结果相互矛盾。实现最佳直方图阈值以区分对象和背景的公认策略是估计两个数据分布并找到它们的交点。这项工作提出了一种基于布谷鸟搜索算法 (CSA) 和广义高斯 (GG) 分布的策略来评估最佳阈值。为了测试这种方法，我们在合成和实际场景中进行了几次实验，并将我们的结果与文献中的其他知名算法进行了比较。这些实际案例包括医学图像数据库和我们自己生成的数据库。模拟环境中的结果显示了所提出的策略相对于其他算法的明显优势。在真实环境中，这在最佳算法中名列前茅，使其成为可靠的替代方案。