# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction
**Texture** feature is used to partition images into ROI and classify those regions as bleeding and non-bleeding. It provides information about an image in the spatial patterns of colors or intensities that repeat. In [75; 116], a conventional texture representation model, named uniform Local Binary Pattern (LBP), was used to differentiate bleeding and normal regions. The study in [35] extracted the texture features (LBP) from suspicious areas in images and their surroundings for classifying bleeding. [PERSON] et al. [38] extracted an LBP based on the contourlet transform as texture features to segment abnormalities in WCE images. As a color texture feature, [PERSON] et al. [12] integrated chrominance moments and uniform LBP to discriminate bleeding regions from normal regions. [PERSON] et al. [88] also extracted texture features (LBP) for segmentation from WCE images in order to prevent false detections. For recognizing bleeding regions, a 6D color texture feature vector {x = (R, G, B, H, S, I)} was developed in [106]. [PERSON] et al. [4] presented bleeding detection system-computed texture features to extract additional information from the captured image frames. By using a histogram of the index image, a distinguishable color texture feature was developed in [53] for automatic bleeding image detection. The Gray-Level Co-occurrence Matrix (GLCM) is a statistical approach for assessing texture that considers the spatial interaction of pixels. The GLCM functions describe the texture of CE images by computing how frequently pairs of pixels with given values and in a specified spatial relationship appear in an image, generating a GLCM, and afterward extracting statistical measures from this matrix. In [117], the authors proposed an efficient normalized GLCM for extracting the bleeding features from CE images. In [118], the authors proposed a texture feature descriptor-based algorithm that operated on the normalized GLCM of the magnitude spectrum of the images for a real-time computerized GI hemorrhage detection system. The study in [72] compared two types of texture features, GLCM and Homogeneous Texture Descriptor (HTD), with various numbers of color histogram bins. [PERSON] et al. [45] extracted texture attributes from the Gaussian mixture model superpixels in WCE images. **Shape:** Shape feature extraction from images involves the process of identifying and describing the geometric characteristics of objects or regions within the image. This includes detecting object boundaries; computing features like area, perimeter, circularity, and eccentricity; and representing the shape using descriptors like chain codes, Hu moments, Histogram of Oriented Gradients (HOG), or Fourier descriptors. Among them, HOG is a popular shape feature extraction method commonly used for object detection and recognition tasks. The studies in [119; 120; 121] specifically utilized the HOG descriptor for shape feature extraction. **Extraction Domain:** According to the taxonomy, the extraction domain is the process of extracting bleeding features from CE images. All of the reviewed studies were categorized into three parts depending on the extraction domain: global feature (when the features are extracted from the whole frames or images); local feature (when the features are extracted at the pixel-level or from a portion of an image -specific block size, ROI, POI);and combined local and global features (when the features are extracted at both pixel and image levels). **Global feature:** The entire image information is used in the global feature extraction technique. Using statistical features (such as mean, mode, variance, moment, entropy, energy, skewness, kurtosis, etc. ), several articles [1,30,61,71,78,79,94,96,103,109,115,116,122] extracted bleeding features from whole CE images. In [50], statistical color features of bleeding images were extracted from the RGB plane's first-order histogram.