# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction
In [78], the RGB image was transformed into HSV color and several statistical parameters, like variance, kurtosis, skewness, entropy, etc., were calculated from the histograms of CE images. The extracted features were applied in a bleeding detection method. The contrast, cluster shade, cluster prominence, and entropy were computed to extract bleeding features from the Gray Level Co-occurrence Matrix (GLCM) in the HSV color space [79]. [PERSON] et al. suggested a pixel-based method for the detection of bleeding regions in CE videos. The HSV color space was utilized to compute the bleeding information [29]. The study in [88] presented a color-based segmentation using the HSV color space to detect bleeding regions in CE images. [PERSON] et al. presented a bleeding detection method based on the HSV color space with dominant color and co-occurrence of dominant colors for feature extraction to classify bleeding lesions [66]. In [67], the HSV color moments were used to extract bleeding features from CE images, achieving the highest accuracy compared to the local binary pattern (LBP), local color moments, and Gabor filter. Using a block-based color saturation approach in the HSV color space, CE images were classified as bleeding or non-bleeding in [68]. A two-stage analysis system was proposed in [69], in which both block- and pixel-based color saturation methods used the HSV color space to extract bleeding features. Color saturation and hue were obtained in the study by converting the input videos or images to the HSV color space. In [70], the authors compared texture features with color features extracted from the HSV color space for the classification of bleeding and showed that color features provided better results. A fuzzy logic edge detection technique was applied in the HSV color space in [1] to extract features of bleeding and non-bleeding from CE images. According to [9], a feature selection strategy was proposed based on HSV color transformation to extract geometric features from CE images for classifying bleeding images. To extract the color features, another research group [70] applied the HSV color space Scale Invariant Feature Transform (HSV-SIFT) for CE abnormality detection. Various statistical features are computed from the hue, saturation, and value channels of the HSV color space. The hue space (H) provides a useful feature for color objects or surfaces. The hue space was utilized in [71] to extract features from CE images for an automatic bleeding detection approach. Another strategy used the combination of a hue-saturation (HS) color histogram with relevant features (64 bins) for extraction of information in order to identify suspected blood abnormalities [72]. The HSI (hue-H, saturation-S, intensity-I) color space is another variation of the HSV color space. A binary feature vector in the HSI color space was more effective in extracting features in a bleeding detection approach [73]. A segmentation approach in which the average saturation from the HSI color space, as well as the skewness and kurtosis of the uniform LBP histogram, were used as features for automated segmentation to detect bleeding in CE images [43]. Another research group suggested an HSI color histogram to follow a moving background and bleeding color distributions through time in the first stage. [PERSON] et al. [74] presented six color features in the HSI color space to classify bleeding and normal CE images. **Other color spaces:** In addition to the most popular RGB and HSV color spaces, a few articles utilized different color spaces to extract bleeding features from CE images, such as YIQ (luminance-Y, chrominance-IQ: in phase-I and quadrature-Q), YCbCr, CIE L*a*b*, CIE XYZ, and K-L (Karhunen-Loeve Transform) color spaces. The study in [89] analyzed only the Q value of the YIQ color scheme to determine the ROI section. Then, a composite space Y.I/Q of the YIQ color space was presented to extract bleeding features by computing the mean, median, skewness, and minima of the pixel values.