## 3 Materials and Methods
### Wound Alteration Classification Model
#### 3.3.2 Feature Extraction  
Once the images were fully segmented, color and texture descriptors were extracted from each segmented region. The combination of textural and color indicators gave a total of 182 features extracted from the dataset, with the help of the _Pyfeats_ library [32].  
Color is one of the most important features of this kind of image because the main alterations that can occur on a post-operative wound while healing is related to the coloration present in the suture's borders. The feature extraction process was performed on multiple color spaces, such as RGB, LAB, Haematoxylin-Eosin-DAB, CIE, XYZ, HSV, YCbCr, YDbDr, YUV, YPbPr, and YIQ [33]; 4 features (mean, standard deviation, skewness, energy) were extracted for each color channel component, giving a total of 132 in all color models. The color features extracted are listed with the corresponding equation in Table 11.  
Whereas color features are a great measure for wound alterations regarding the redness of infection, other alteration indicators are hardly measured with color features. Purulent discharge, swelling, and exudation are important characteristics that represent a change in the texture of the surgical site. Accordingly, other types of features are necessary to further differentiate between these alterations, such as textural features.  
Textural features can be divided into statistical and structural. Three types of statistical features were extracted from the wound regions: first-order statistics (FOS), features derived from gray level co-occurrence Matrix (GLCM), and local binary pattern (LBP). These features describe the texture as a measure of low-level statistics of gray-level images. The FOS features extracted from the image converted into grayscale are represented in Table 14. The GLCM is a second-order statistical feature that uses a dependency matrix based on the relationship among the gray levels. GLCM, which was proposed by [34], is based on the estimation of the second-order joint conditional probability density function. For a chosen distance of one, four angles (\(\pi\), \(\pi/4\), \(\pi/2,3*\pi/4\)) were considered to build the corresponding matrices. Hence, for each feature, four values are obtained in which the mean and range of each parameter are calculated. These averaged mean and range of the four values comprise a set of 28 features. The corresponding equations are displayed in Table 15. LBP is a simple and robust method that describes textural information. It is computed by analyzing the circular neighborhood of radius R surrounding a central pixel P. In binary encoding, all the pixels with gray values less than P are encoded as 0 while the others are encoded as 1. These binary-coded values are converted to decimal numbers for building a histogram, thus obtaining a feature vector [35]. LBP features were obtained by choosing circles with a different radius around the central pixel and constructing separate LBP histograms. Energy and entropy of the LBP correspond to the final features, constructed over different combinations of radius and pixel count. The different sequences of points and radius (R = 1, 2, 3 with corresponding pixel count P = 8, 16, 24) constructed 6 features.