This chapter introduces the reader into the problem of image segmentation using Metaheuristic Algorithms and presents the intuition of the principal concepts relevant to this book, starting with the segmentation of digital images which is a family of methodologies whose objective is to obtain underlying structures present on pictures to facilitate the interpretation of the content at a high level; for example, to obtain borders or grouping of pixels that form regions with some common property. Within the family of segmentation methods, there is a process known as thresholding. The goal of image thresholding is to separate groups of pixels through the optimal selection of specific pixel values (thresholds) to partition the image. To avoid an exhaustive search, the selection of threshold values can be reformulated as the minimization or maximization of a function, where optimization tools are essential. From all the optimization methods available on the literature, Metaheuristic Algorithms have been widely used for image thresholding with important results.