Abstract
Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.
Highlights
Gastrointestinal tract (GIT) diseases, such as ulcer, bleeding, Crohn’s disease, cancer or chronic diarrhea are common nowadays
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
Once the examination is complete, the images can be downloaded to a dedicated computer from data logger (DL) and inspected by clinical experts through specific software
Summary
Gastrointestinal tract (GIT) diseases, such as ulcer, bleeding, Crohn’s disease, cancer or chronic diarrhea are common nowadays. 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. 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. Clinicians have to go through each frame manually, leading to visual fatigue This tedious and time-consuming process is the main drawback of WCE. On the other hand, is more efficient for chronic cases In this particular work, we have extracted color features for various color spaces.
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