# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction
Based on the YIQ color histogram, another article proposed an automatic bleeding detection scheme for CE images [90]. The YCbCr color space was presented to collect information from CE images in order to identify images with lesions [91]. [PERSON] et al., investigated several color histograms, including RGB, HSV, YCbCr, and CIE L*a*b*, and proposed the YCbCr color space to extract bleeding features for discrimination of bleeding images from normal CE images [92]. The studies in [93; 94; 95; 96] suggested using the CIE L*a*b* color space for detection or localization of the bleeding region in CE images. [PERSON] et al. proposed a bleeding zone detection system based on the contourlet transform in the CIE XYZ color space [97]. Another feature extraction color space was the Karhunen-Loeve (K-L) Transform, which was utilized for fuzzy region segmentation in CE images [98]. The study by [PERSON] et al. [99] proposed a computer-aided bleeding and ulcer detection approach based on the covariance of second-order statistical features in the K-L color space. A K-means color group was suggested as a color feature extractor for superpixel segmentation to find bleeding regions in CE videos [100]. **Combined multiple color spaces:** To detect bleeding, a group of color features was computed using multiple color spaces in CE images. The method described in [26] employed two distinct enhancing operations for identifying bleeding in CE images: the first was for the RGB color space, and the second was for the grayscale color space. The study in [101] determined the ROI of bleeding CE images using the YIQ color space and extracted features from the ROI using the CMYK color space. Based on the RGB and HSV color spaces, CE images were defined using statistical characteristics to extract bleeding features [33; 102]. A 9-D feature was extracted at the superpixel level from the RGB and HSV color spaces during the segmentation stage of the study [103]. Some studies utilized a combination of the RGB and HSI color spaces to extract features for the bleeding detection approach in CE images [104; 105; 106; 107]. Five color spaces (RGB, HSV, CIE L*a*b*, YCbCr, and CMYK) were used to extract features in [108]. In [7], the authors proposed the R channel with respect to the G and B channels and the ratio of G and B channels as features in the RGB color space, and the HSV color space was chosen for the saturation feature. The color features were extracted using the color components X = {R, G, B, L, a, b, H, S, V, F1, F2, F3} in the RGB, CIE L*a*b*, and HSV color spaces from each superpixel of CE images [109]. In [110], the color components H, S, a, b from the HSV and CIE L*a*b* color spaces and the Ros (Rosenfeld-Troy) metric were used. Ten features, including Normalized Excessive Red (NER), Hue, sum RGB, chroma, etc., were used to analyze CE video frames in [111]. For the segmentation of bleeding regions from bleeding CE images, delta E color differences were used to extract features by applying nine color shades (red, orange, brown, maroon, purple, pink, mahogany, brown, and bittersweet) for characterizing different types of bleeding [45]. The recommended Probability Density Function (PDF) fitting-based feature extraction technique was used in the YIQ, HSV, and CIE L*a*b* color spaces [112]. In [113], 40 features were extracted from five different channels, including R in the RGB color space, V in the HSV color space, Cr in the YCbCr color space, and a and L in the CIE L*a*b* color space. An article [114] investigated 21 color components from RGB, YUV, YIQ, HSB, CIE XYZ, and CIE L*a*b* color spaces for feature extraction, such as U/Y, V/Y, I/Y, and Q/Y. [PERSON] et al. [115] demonstrated a combination of the HSV and YIQ color spaces using normal PDF to detect GI diseases in CE videos. According to [30], the HSV color space was used for threshold analysis of the classification model, and the CIE L*a*b* color space was used in the trainable model for edge detection in images.