# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 1 Introduction  
Gastrointestinal tract (GIT) diseases, such as ulcer, bleeding, [PERSON]'s disease, cancer or chronic diarrhea are common nowadays. Bleeding and ulcer are some common lesions which affect the small and large bowel. In the United States, approximately 1.6 million Americans currently are currently suffering from inflammatory bowel disease (IBD), representing an increase of about 200,000 since 2011.There are approximately 70,000 new cases of IBD diagnosed each year, and there may be as many as 80,000 children who are suffering from [PERSON]'s disease (CD) or ulcerative colitis (UC) currently. Additionally, as illustrated reported in the first paper of this issue [1], the incidence number of occurrences of IBD is increasing worldwide [2]. The growth of IBD cases in newly-industrialized countries has paralleled its growth on par with that of the Western world 30 to 40 years ago. Genetic and environmental studies performed in these countries may provide new clues to the pathogenesis of IBD. However, it adds another layer of complexity since risk factors and gene-environment interactions may vary by continents and ethnicities [3]. Traditional endoscopy has been adopted for many years in order to diagnose abnormalities of GIT, whereby a physician controls a flexible endoscope to examine the lower and upper parts of GIT. This technique is limited to inspecting bowel of average length 7-8 m. It imposes high level of discomfort on the patient as well.  
Wireless capsule endoscopy (WCE) [4; 5] is a recent technology introduced by Given Imaging Ltd. (Yokne'am Illit, Israel) to visualize the entire GIT painlessly. It offers an efficient and comfortableway for visualizing the complete GIT. It has eight skin antennas mounted to the abdomen wall. While moving through the complete GIT, the capsule captures numerous images at approximately 2-4 frames per second (fps), and transfers them wirelessly to the data logger (DL) or recorder unit. This DL is hooked up to the patient waist and the videos/images are stored. Once the examination is complete (i.e., the WCE exits patient's body after 8 h), the images can be downloaded to a dedicated computer from DL and inspected by clinical experts through specific software. This procedure produces more than 60,000 images per examination and experts spend about 4-5 h to inspect the whole video footage very carefully. In some mild cases, clinicians have to go through each frame manually, leading to visual fatigue. This tedious and time-consuming process is the main drawback of WCE. Various image processing techniques for automatic disease detection (based on size, shape, and depth, including performance matrices) have been developed. Automatic lesion detection, on the other hand, is more efficient for chronic cases.  
In this particular work, we have extracted color features for various color spaces. We have analyzed each band in order to separate the ulcer and non-ulcer pixels. Furthermore, it was combined with cross-correlation measure in order to add similarity between two images or matrices. Classification task was performed using support vector machine with and without grid search method in order to quantify the result. The main contribution of this work is the implementation of a novel computer-aided diagnostic method which is used to discriminate ulcer pixels from non-ulcerated ones with high performance in terms of sensitivity, specificity, and accuracy.  
This paper is organized in the following manner: Section 2 describes the research background and the related works. Section 3 demonstrates the methodology, and Section 4 describes experimental setup, results, and discussions. Section 5 concludes the current work.