# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction
Therefore, the model was unable to eliminate a few very small judged bleeding zones that were not bleeding. To address this issue, cluster of pixels-based feature extraction techniques have been used in some research to extract features from bleeding CE images. A cluster of pixels in the RGB color space was utilized instead of single pixels in an automatic bleeding classification system, which improved the sensitivity [55]. Instead of directly using the RGB color space, a G/R composite color plane was utilized to extract features from CE images in [36]. In other research, [PERSON] et al. [39] extracted statistical features from the overlapping spatial blocks in CE images based on the G/R color plane. The R/G transform color plane pixel intensity ratio was utilized for the extraction of bleeding information from CE images [56]. Rather than considering individual pixels, [PERSON] et al. considered the surrounding neighborhood block of the individual pixel and the R/G plane ratio for bleeding feature extraction from CE images [57]. [PERSON] et al. [40] used a temporal red-to-green ratio (R/G) feature value to detect bleeding regions. According to [PERSON] et al. [58], the average pixel intensity ratio in the RGB color space was used to extract features from CE images for an automatic bleeding detection approach. The study in [59] presented rapid bleeding detection in CE videos. The red ratio (RR) in the RGB color space was used to extract a feature from each superpixel of CEimages. Also, the RR feature for individual pixels was utilized for feature extraction of bleeding from CE images [25]. The various coefficients of the RGB color space for bleeding and non-bleeding superpixel blocks are RG (Red, Green), RB (Red, Blue), and GB (Green, Blue) in two-dimensional space. [PERSON] et al. [60] presented an automatic gastric hemorrhage detection system based on the coefficient of variation in the RG two-dimensional color space for different superpixel blocks in CE images. Another transformation form of the RGB color space is the OHTA color space. In [61], the OHTA color space was utilized to extract the features of bleeding from CE images. A custom RGB color space was proposed in [31], which was similar to the CMYK color space and was used to extract features for automatic blood detection in CE videos. [PERSON] et al. [62] presented a normalized RGB color space histogram-based feature extraction method to identify bleeding in CE images. Another research group employed a two-stage saliency map extraction method to localize the bleeding areas in CE images. The first-stage saliency map was constructed using a color channel mixer, and the second-stage saliency map was derived from the RGB color space's visual contrast [63]. Some studies applied algorithms in the RGB color space to extract features from CE images or videos. Using advanced pattern recognition techniques, a MapReduce framework was presented for the identification of bleeding frames and segmentation of bleeding zones. For classification, the system encoded RGB color space information from the raw data of CE images using a K-means clustering algorithm, and for segmentation of the bleeding zone, a density-based algorithm (DBSCAN) was utilized [64]. [PERSON] et al. presented an automatic bleeding region detection system using the Expectation Maximization (EM) clustering algorithm in the RGB color space [65]. **HSV**: HSV (hue-H, saturation-S, value-V) is an alternative representation of the RGB color space that correlates better with the human perception system. The HSV color space is generated from cartesian RGB primaries, and its components and colorimetry are related to the color space from which it is derived. Several studies [1,11,29,35,43,46,66,67,68,69,70,80,81,82,83,84,85,86,87] employed the HSV color space histogram as the color feature descriptor to extract features from CE images or videos in automated bleeding detection systems.