Paper Info
Title
Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video.
Abstract
Wireless capsule endoscopy (WCE) enables non-invasive and painless direct visual inspection of a patient's whole digestive tract, but at the price of long time reviewing large amount of images by clinicians. Thus an automatic computer-aided technique to reduce the burden of physicians is highly demanded. In this paper, we propose a novel color feature extraction method to discriminate the bleeding frames from the normal ones, with further localization of the bleeding regions. Our proposal is based on a twofold system. First, we make full use of the color information of WCE images and utilize K-means clustering method on the pixel represented images to obtain the cluster centers, with which we characterize WCE images as words based color histograms. Then we judge the status of a WCE frame by applying support vector machine (SVM) and K nearest neighbor (KNN) methods. Comprehensive experimental results reveal that the best classification performance is obtained with YCbCr color space, cluster number 80 and the SVM. The achieved classification performance reaches 95.75% in accuracy, 0.9771 for AUC, validating that the proposed scheme provides an exciting performance for bleeding classification. Secondly, we propose a two-stage saliency map extraction method to highlight bleeding regions where the first stage saliency map is created by means of different color channels mixer and the second stage saliency map is obtained from the visual contrast. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas. Quantitative as well as qualitative results show that our methods could differentiate the bleeding areas from neighborhoods correctly.
Year
DOI
Venue
2016
10.1109/JBHI.2015.2399502
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
bleeding classification and region detection,wireless capsule endoscopy,words based color histograms
Computer vision,Histogram,Pattern recognition,Color histogram,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Pixel,Cluster analysis,Channel (digital image),Color image
Journal
Volume
Issue
ISSN
PP
99
2168-2208
Citations 
PageRank 
References 
19
0.94
12
Authors
3
Name
Order
Citations
PageRank
Yixuan Yuan114329.34
Baopu Li234830.88
Max Q.-H. Meng31477202.72