Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

Abstract

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.

The future of capsule endoscopy in clinical practice: from diagnostic to therapeutic experimental prototype capsules

“…Figure 1 gathers the results from the previously presented studies, which focus on the ulcer detection. Suman et al [55] reported the best results compared to the other methods, achieving an accuracy of 97.9%. However, the results cannot be directly compared to the other methods because different datasets were used.…”

“…In this context, the majority of recent studies were based on combinations of texture and colour fea-tures to discriminate ulcers from normal tissues. In the study of Suman et al [55], the colour components of seven different colour spaces were analysed in order to find an optimal combination of their colour components that better discriminates ulcers from normal tissues. The selected components were Cr (which represents the difference of red from a reference value) from YCb-Cr colour space, the yellow (Y) component from CMYK colour space, and the blue (B) component from RGB.…”

Bleeding detection in wireless capsule endoscopy videos — Color versus texture features

“…In our previous work, we have developed an algorithm to extract color feature for ulcer using statistical feature analysis. 23 .…”

Smart Healthcare

“…In [22], Suman et al reported their work on medical image processing for wireless capsule endoscopy. They introduced a novel statistical approach to differentiating ulcer and non-ulcer pixels using color bands as the feature vector and the support vector machine with grid search as the classifier.…”