## 1 Introduction
[17] used Fully Convolutional Networks to segment and detect corners in aerial images of buildings. Their work achieved good performance in this task, outperforming several algorithms in their corresponding comparison. The region extraction approach splits an image into regions using merging or dividing techniques. [PERSON] et al. [18] proposed an image segmentation method using edges as first elements, incorporating pixels into each region at each iteration. A similar technique was used by [PERSON] et al. in 2003 [19] using local entropy, getting good performance with noisy images. The use of a region extraction algorithm is convenient because a region comprises more pixels and achieves better performances with noisy images, providing more information than edge detection techniques [18], which face issues with noisy images. Combining both region extraction and edge detection provides more detail information about the image [18]. Clustering or feature thresholding consider choosing a threshold value that maps all the pixels into different clusters. These threshold values can be grey level, gradients or percentages. [PERSON] et al. [20] used an algorithm based on clustering to segment fish images. [PERSON] [21] reported a similar technique to find white cells using a k-means algorithm. And [PERSON] and [PERSON] [22] used a clustering algorithm based on adaptive k-means, diversifying the parameters to boost image segmentation's performance. In general, clustering is easy to implement, simple, and the results are easy to interpret. On one hand, the extraction of regions seeks to identify the region of interest for further analysis. On the other hand, clustering algorithms seek to group regions with similar characteristics regardless of whether they represent something relevant. The clustering approach for segmentation is the protagonist in this work. There are recent works on the detection of breast cancer using image processing and deep learning to achieve good results in both branches. In 2018, [PERSON] et al. [23] performed a comparative study of several algorithms for breast cancer detection using Infrared Thermal Imaging, obtaining relevant results in this field. One of the classic methods for image thresholding is the Otsu method [24], which uses clustering to generate binary images. This method is still one of the most referenced thresholding methods because of its simplicity and good performance. This method is sensitive if the region of interest (ROI) or the background is bigger than the other region since it will classify the other pixels incorrectly. The Otsu method finds the optimal threshold by maximizing or minimizing variances between or within each class, respectively [25]. This method has been successfully used for image thresholding in many applications [26; 27; 28]. In this paper, we explore two algorithms based on populations, the novel bat algorithm (NBA) and the genetic algorithm (GA). In addition, we explore the simulated annealing algorithm (SA) based on a single trajectory. The GA is based on an evolutionary system and the NBA is based on swarm optimization. There are a lot of segmentation techniques, but there is no method to determine which is the most useful algorithm for the segmentation of specific digital images. In this paper, we focus on meta-heuristics applied to the segmentation of digital images, specifically, mammography images. This paper is organized as follows. The review of recent works in mammography image segmentation is presented in Section 2, with an explanation of the algorithms under test, while giving insights into their functioning. In Section 3, the proposed methodology is described. Section 4 provides the results and the analysis of the experiments. Finally, in Section 5 we present the discussion and conclusions of this work and future work in this area.