# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 2 Background and Literature  
Researchers have ventured into the technology of using automated computer-aided design (CAD) tools, such as WCE for ulcer screening. Precious clinical information in some important lesion areas can be displayed on an image [6; 7; 8; 9]. [PERSON] [6] has used Retinex theory and the salient region detection method for various pathologies, such as stenosis, chylous cysts, lymphangiectasis, polypoid, bleeding, angioectasia, and ulcers. They have used a color enhancement method to improve the diagnostic yield of the CAD system. A significant improvement in detection performance using the Retinex-based color enhancement method has been achieved. An unsupervised method [10] has been used to localize the region-of-interest in order to detect angioectasia. The utility of IHb index for angioectasia detection has been pioneered to detect other abnormalities.  
[PERSON] [8] has used a saliency detection method based on multi-level super pixel representation in order to outline the ulcer candidates. They have evaluated the corresponding saliency in accordance with the texture and the color feature of each level in the super pixel region. The images have been categorized by using saliency max-pooling with locality-constrained linear coding (LLC).  
[PERSON] [9] has presented a method to detect various abnormalities in GIT by considering color as a discriminative feature. This method has been tested on a WCE model. Here, the single image (instead of the complete WCE video footage) has been analyzed. The author [11] has reviewed some current CAD methods employed in enhanced video capsule endoscopy. Various hardware and software problems have been highlighted in a review article [12] for detecting lesions in the small bowel.  
Peptic ulcers are usually found in the duodenum (duodenal ulcer) and in the stomach (gastric ulcer). They can be found in small and large bowel areas of the GIT as well. They may cause severe gastrointestinal perforation or gastrointestinal bleeding [13]. Usually, it appears as a white spot in WCE images. In severe cases, these ulcers are accompanied by bleeding and other abnormalities. Ulcer lesions and normal tissue can be differentiated by using color and texture features (see Figure 1).  
Feature selection [14] as practiced in machine learning is beneficial for supporting the WCE findings [15; 16; 17]. In general, feature extraction involves creating many new features mixed with the current ones. Due to the fact that the characterization of complete data variance is not possible after dimensional reduction, a highly-discriminative selecting feature [10] is undoubtedly necessary.  
In order to classify the area of interest for capsule endoscopic images, first-order-histogram-based features extracted from various color spaces are very important. Relevant information can be extracted using various color spaces in order to describe the pattern of assured class. In case of bleeding detection, different color spaces [18] such as RGB (Red, Green, Blue), CMYK (Cyan, Magenta, yellow and Key [Black]), YIQ (Luma, chrominance information), CIE Lab, and HSI have been extensively investigated.  
[PERSON] [13] used an improved CH algorithm (chosen name for the color coherence vector, CCV) to extract the color feature. The grey level co-occurrence matrix (GLCM) has been used to extract texture information. In our previous work [19], RGB and Lab color spaces have been chosen for statistical analyses of ulcer and non-ulcerated pixels. This paper extends the work of [19] by incorporating various color bands for statistical analysis.  
[PERSON] et al. [20] has proposed a geometry-based automatic colorectal polyp detection method that has motivated the current work. The authors [21] have proposed a new computer-assisted bleeding detector for differentiating between bleeding and non-bleeding regions. The utilized second component from the CIE Lab color space with enhancement and segmentation techniques involves anisotropic diffusion.