# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 2 Materials and Methods
### Textural Features  
Having established the best segmentation software among those previously described, we moved on to the extraction of the textural features. When attributing features to an image, it is essential to examine its pixel-by-pixel structure and consider the interactions among different pixels as observed through the lens of grayscale intensity. Each region of a given image can be depicted by both external and internal features, representing the characteristics of edges and the constituent pixels of the region, respectively. In the case of FFDM images, the primary objective is to extract information related to pixel intensity. Texture analysis, lacking a formal definition, relies on intuitive descriptions for features such as smoothness, coarseness, and regularity. All these properties were related to the intensity of the pixels present within a certain region or of the entire image, highlighting areas of non-uniformity. Therefore, the estimation and classification of different textures were performed thanks to the texture analysis, which consists of the extraction of textural features. Texture analysis has been applied in the analysis of digital images in many fields, enjoying particular success in the field of radiomics [15]. The literature offers a large number of features that can be extracted from an entire image or by appropriate ROIs. \"Texture detection\" means a process that separates, finding edges, and areas with different textures on the image; the number of features taken into consideration affects how accurately the distinction is made between pixels, and therefore, with it, existing diversities can be recognized in different breasts. As already mentioned, the extraction methods for texture analysis can be divided into two types:  
* Statistical/stochastic approach;
* Structural approach.  
The first, as the name suggests, treats textures as statistical phenomena, i.e., the formation of a texture on the image is described by statistical properties such as pixel intensity and location. The level of intensity at a certain point of the image is strongly dependent on the intensities of nearby points, unless the image is not made up only of random noise [37]. Statistics based on co-occurrence matrices and histograms [38; 39] are the simplest examples of the statistical measurements of textural features. The second category, which is based on a structural approach, introduces the concept of texel, which is considered the fundamental unit of the map textures. This means that with this second approach, more stress is placed on the structure, the spatial dimension of a texture, considering textures as vectors of texels that make up the texture space, just as we consider the image to consist of vectors of pixels. In any case, each texture contains both structural and statistical characteristics; therefore, in this study, both approaches were considered in parallel [40]. The features to be extracted from the breast, chosen based on the literature by the most recent studies [41], can be summarized as follows:  
* 1-112 features of Haralick;
* 113-157 features of law;
* 158-185 features run length;
* 186-215 features of wavelet;
* 216-227 features of the histogram;
* 228 fractal dimension;
* 229 local binary pattern.  
Then, a total of 229 statistical and morphological features describing the texture of the region of interest were extracted.