# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction  
Features are a major part of any pattern recognition task. Feature extraction is the process of converting raw data (like an image) into a set of features. It aids in reducing the number of resources required to explain big amounts of data. Color and texture are two common and crucial image recognition properties. Both are also highly beneficial in extracting features from CE images to identify bleeding because bleeding areas have more or fewer color differences and/or textures compared with their neighboring environment. The feature extraction domain is a selection process that selects a region of the image that is used to efficiently extract features. Various feature extraction domains were used to accurately extract bleeding features, such as ROI, specific block, pixel level, and image level. **Color Space:** Color space refers to a specific color arrangement. There are many different color spaces, such as RGB, HSV, YIQ, YCbCr, CMYK, CIE L*a*b*, CIE XYZ, etc. From the literature review, the color spaces used for feature extraction were categorized into four groups: RGB, HSV, Other, and Combined color spaces. **RGB:** Images are represented in the RGB color space as an m-by-n-by-3 numeric array, the components of which indicate the intensity levels of red, green, and blue color channels. The range of numeric values is determined by the image's data type. Different types of the RGB color space are available, such as linear RGB, sRGB (standard red, green, blue), adobe RGB, and so on. In CE images or videos, a bleeding zone is distinguished by the presence of a bright red or dark red zone. Many studies utilized the RGB color space to extract features for the identification of bleeding images or regions from CE images. The studies in [47; 48] presented an automated obscure bleeding detection technique on the GI tract based on statistical RGB color features that could classify bleeding and non-bleeding images in CE images. By using the same RGB components of each pixel of the CE image, the study in [49] presented a system to automatically detect bleeding zones in CE images. From the first-order histogram in the RGB plane, the approach extracted bleeding color information from CE image zones by calculating the mean, standard deviation, skew, and energy [50]. [PERSON] et al. presented a two-dimensional color coordinate system in the RGB color space to segment abnormality in CE videos. The approach combined two descriptors to extract features: the first was based on image color content, while the second was based on image edge information [38]. Another research group proposed using color vector similarity coefficients to evaluate the color similarity in the RGB color space in order to detect bleeding in CE images [51]. [PERSON] et al. presented a method using color spectrum transformation (CST) for the identification of bleeding in CE images. This approach included a parameter compensation step that used a color balance index (CBI) in the RGB color space to compensate for irregular image conditions [52]. The study in [53] suggested an automatic bleeding image detection technique utilizing an RGB color histogram as a feature extractor and bit-plane slicing to detect bleeding and non-bleeding images from CE videos. In [54], the authors utilized superpixel segmentation in RGB color format to extract bleeding information for an automatic obscure bleeding detection technique. In [21], an automated bleeding detection approach was presented using a color-based per-pixel feature extraction technique. [PERSON] et al. [37] presented an automatic bleeding detection approach based on an RGB color histogram of block statistics to extract features from CE videos. To reduce computational complexity and flexibility, the approach utilized blocks of surrounding pixels rather than individual pixel values. The conventional machine learning model (which includes different stages: image acquisition, pre-processing, feature extraction, and classification) used single pixels for the training and testing data.