# Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of _Drosophila melanogaster_
## 1 Introduction  
### Context  
Nowadays, image processing is used in many different fields such as medical images, object detection and face recognition. This discipline focuses on the analysis and treatment of images to improve their quality and extract information from them [1]. An essential technique of image analysis is segmentation, which plays an important role on many applications from picture editors to image-based diagnostic systems.  
Segmenting an image consists in dividing it into regions or objects so that it can be analysed afterwards [2]. Thus, pixels are categorised in different connecting regions depending on their grey scale. From a classic point of view, the grey scale of pixels can be categorised for edge detection or object detection [3]. The methods to detect edges use properties of the grey scale of pixels related with differences colour distinctness and texture variation [3]. These characteristics describe objects which are in boundaries of regions.  
To discover other objects, methods based on the similarity of the grey scale are used, which are classified in three groups: region-based segmentation, watershed segmentation and threshold segmentation [4]. The first group consists in grouping the pixels of an image into homogeneous regions using a criterion [4]. Usually, properties such as colour,texture and intensity establish the characteristics of a pixel to be included in a region. The simplest form of region-based segmentation is region growing. In this variant, the pixels are clustered into superior regions using an existing criterion: a set of points called seeds. These points determine the characteristics of a pixel to be included in a region. For instance, an implementation of this variant is LimeSeg [5]. Alternatively, another form of segmentation based on similarity grey scale is the watershed algorithm. In this case, the image is conceived as a topographic relief where the grey scale of pixels represents the heights of the plane, which is useful to define the contour of the background or to separate objects [6].  
Finally, there is another group based on grey scale similarity, called threshold segmentation. This consists in splitting the pixels of an image depending on one or more threshold values based on the histogram of their grey scale [7]. For instance, a single threshold uses only one threshold to distinguish the background of an image from their objects. If it is necessary to segment the objects in different groups, multiple threshold segmentation is applied, but it is a costly process [8].  
All these methods have been applied to analyse many different types of images such as numbers, faces or objects with correct results. However, segmentation is still a challenge in biological images, specifically, their difficulties lie in their acquisition conditions, such as noise from the microscopes, low resolution and low frame rate as well as the characteristics of the objects, for example, brightness, unclear boundaries and overlapping objects [9]. An efficient segmentation method of biological images will have to overcome all these problems considering that a high level of quality and accuracy is necessary for all of them [10].  
Efficient segmentation methods of biological images work well on one data set of biological images, but if they are applied to data sets with cells from different organs and tissues, their performance can be significantly reduced [11]. Therefore, they need to manually readjust their parameters for new images to obtain an accurate segmentation [12], requiring constant supervision which delays research projects. Thus, each time a different biological image is segmented, an optimisation problem appears around these values, which are dependent on the segmentation method [13; 14]. Owing to this problem, machine learning approaches have been developed to generalise and automatise the process of segmentation [15].