# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 4 Feature Extraction
In another study [79], statistical features were measured from GLCM after applying an Undecimated Double Density Dual Tree Discrete Wavelet Transform on CE images. [PERSON] et al. [74] applied six color statistical features to identify bleeding features from the full image feature. [PERSON] et al. [73] utilized color information to extract the bleeding features from the image feature. In [9], color, shape, and surf were used for feature extraction from whole images. **Local feature:** A pixel-level feature extraction approach was proposed in several studies in order to accurately identify bleeding images [29,36,51,56,58,120,121,123,124]. Instead of computing different features from each pixel, a few researchers proposed block-based local feature extraction techniques to reduce time and computational cost [37,47]. The study in [39] investigated various overlapping block sizes (3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, and 9 \(\times\) 9) and proposed a 7 \(\times\) 7 block size to extract features from CE images. [PERSON] et al. [80] divided the original image of 512 \(\times\) 512 pixels into 256 sub-images with a resolution of 32 \(\times\) 32 pixels for feature extraction. In [70], the 576 \(\times\) 576 pixels input was sliced into nine non-overlapping blocks, each with 64 \(\times\) 64 pixels. Another research group divided each CE image into blocks of 64 \(\times\) 64 pixels and analyzed the 64 \(\times\) 64 = 4096 pixels in each block to recognize hemorrhage [69]. CE images are surrounded by a large black background, which provides unwanted features. As a result, this reduces the performance of the model. To address this, a few studies introduced a Region Of Interest (ROI) for proper feature extraction. In [92,100,119], the authors selected an ROI from a maximum square inside the circular CE image without loss of main information. The ROI was 180 \(\times\) 180 pixels in size, chosen from a total of 256 \(\times\) 256 pixels. An elliptical ROI was selected inside the image to extract local features in [33]. According to [101], an ROI of the bleeding CE image was determined using the YIQ color space. After that, CMYK values were computed within the ROI pixels, which were applied to discriminate bleeding and non-bleeding pixels. An ROI was selected based on the Q-value of the YIQ color space and a composite space Y.I/Q was used to capture the bleeding information from the ROI section of the CE images [89]. The Pixel Of Interest (POI) technique can also extract local features that depend on the intensity values of pixels. In [112,115], the authors utilized POI instead of whole CE images to extract features for the classification of bleeding images. **Combined local and global features:** Studies used local and global features to develop robust and accurate computer-aided bleeding detection systems. The studies in [65,106,107] proposed bleeding detection application software that was tested at the pixel and image levels. Region-level block-based and image-level global feature extraction techniques were applied in [39] to identify bleeding images. There were two stages presented in [4]; the first used only local color features to categorize bleeding images, while the second included global texture and color features to classify bleeding pixels. In [37], a block-based local feature extraction technique was presented, and then global features were extracted using a color histogram to classify bleeding and non-bleeding images. [PERSON] et al. [57] presented an approach that used the maximum pixel value of each proposed spatial block and the global features to classify bleeding images. In another study [59], the authors used pixels to remove the edge zone and grouped pixels adaptively based on the red ratio in the RGB color space for superpixel segmentation. Another study proposed a global feature descriptor based on magnitude spectrum entropy and a local textural descriptor based on the contrast, sum entropy, sum variance, difference variance, and difference average, operating on the normalized GLCM [118].