Automatic Bleeding Frame Detection in the Wireless Capsule Endoscopy Images
Refereed conference paper presented and published in conference proceedings

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AbstractWireless capsule endoscopy (WCE) is a revolutionary imaging technique that enables direct inspection of the gastrointestinal tract in a non-invasive way. However, viewing the large amounts of images is a very time-consuming and labor intensive task for clinicians. In this paper, we propose an automatic bleeding detection method in the WCE images. 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 in the RGB color space. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas in the WCE images. Then we extract statistic color features in the corresponding saliency region and non-saliency region respectively and fuse them together to represent the whole WCE images. Finally Support Vector Machine (SVM) is applied to carry out the experiment on 800 sample WCE images. Experiment result achieves an accuracy of 95.89%, sensitivity of 98.77% and specificity of 93.45%. This inspiring result demonstrates that the proposed method is very effective in detecting bleeding patterns in the WCE images. Our comparison studies with several state-of-the-art bleeding detection methods confirm that the proposed method achieves much better results than those of the alternative techniques.
All Author(s) ListYuan YX, Meng MQH
Name of ConferenceIEEE International Conference on Robotics and Automation (ICRA)
Start Date of Conference26/05/2015
End Date of Conference30/05/2015
Place of ConferenceSeattle
Country/Region of ConferenceUnited States of America
Detailed descriptionIEEE
Pages1310 - 1315
LanguagesEnglish-United Kingdom
Keywordsbleeding frame detection; statistic color features; two-stage saliency map; Wireless capsule endoscopy
Web of Science Subject CategoriesAutomation & Control Systems; Computer Science; Computer Science, Artificial Intelligence; Engineering; Engineering, Electrical & Electronic; Robotics

Last updated on 2021-15-09 at 04:29