Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis

Abstract

Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task.

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

“…Precious clinical information in some important lesion areas can be displayed on an image [6][7][8][9]. Deeba [6] has used Retinex theory and the salient region detection method for various pathologies, such as stenosis, chylous cysts, lymphangiectasis, polypoid, bleeding, angioectasia, and ulcers.…”

“…Yuan [8] has used a saliency detection method based on multi-level super pixel representation in order to outline the ulcer candidates. They have evaluated the corresponding saliency in accordance with the texture and the color feature of each level in the super pixel region.…”

A Survey on WCE Abnormality Detection Methods

“…Moreover, instead of calculating the response of each descriptor to a fixed number of code words, SALLC method projects each descriptor into an adaptive determined number of coding bases, which is calculated based on the distances between the feature and the visual words. Yixuan Yuan et al [2] proposed a two-staged fully automated computer-aided detection system to detect ulcer from WCE images. In the first stage, an effective saliency detection method based on multi-level super pixel representation is carried out to outline the ulcer candidates.…”

Saliency Aware Locality Constrained Linear Coding for WCE Multi Abnormality Detection based on SURF

“…Simultaneously these images are then sent wirelessly to a data-recorder attached to the patient's waist and the small device is propelled through peristalsis. At last the physicians review the images that are downloaded to a computer, and diagnose various abnormalities in the gastrointestinal tract [5], [6]. An experienced clinician spends an average of 2 hours for the video reviewing and analysing of approximately 60,000 images that are captured per examination of one patient [7].…”