A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Abstract

Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…Among them, HOG is a popular shape feature extraction method commonly used for object detection and recognition tasks. The studies in [ 119 , 120 , 121 ] specifically utilized the HOG descriptor for shape feature extraction.…”

“…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 × 180 pixels in size, chosen from a total of 256 × 256 pixels.…”

“…SVM is one of the most popular supervised ML algorithms that is used to detect bleeding and non-bleeding images or zones from CE images or videos. The majority of studies used SVM based on the extracted features of input images including color space and texture [ 119 , 122 ]. In 2008, Liu et al [ 48 ] developed an automated obscure bleeding detection technique for the GI tract that could classify bleeding and non-bleeding CE images using the SVM algorithm.…”

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution

“…Most of these algorithms have very specific objectives, such as focusing only on selected abnormalities, discriminating various anatomical and luminal findings or improving CE image quality. [ 8 9 10 11 12 ] The source of the datasets used in existing studies varies, but can be roughly grouped into studies that use open-source data alone and those that use only a collected dataset specific to a locale. [ 8 9 ]…”

Retracted: A Computer‐Aided Method for Digestive System Abnormality Detection in WCE Images

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”