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.
Highlights
Wireless Capsule Endoscopy (WCE) is an imaging device to capture video frames from the digestive system. is technology is a noninvasive tool that has many advantages over other methods such as small-bowel imaging that is inaccessible by other traditional endoscopy methods [1]
We propose a new method based on joint normal distribution, regardless of its color, texture, and shape, to extract distinct areas in WCE images as region of interest (ROI)
We applied the proposed method to the normal class and the other pathological findings classes showing the ability of the proposed method in abnormality detection in WCE images
Summary
Wireless Capsule Endoscopy (WCE) is an imaging device to capture video frames from the digestive system. is technology is a noninvasive tool that has many advantages over other methods such as small-bowel imaging that is inaccessible by other traditional endoscopy methods [1]. E captured video usually contains about 8000 frames [3]. Since these lesions appear in few frames of a video and usually have a small size compared to the frame size, physicians may miss them during the examination [4]. It is a time-consuming and boring task for the physician to check a thousand frames to find pathological lesions [5]. It is a time-consuming and boring task for the physician to check a thousand frames to find pathological lesions [5]. erefore, a computer-aided method is needed to automatically detect frames containing lesions
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