Real-Time Image Analysis of Capsule Endoscopy for Bleeding Discrimination in Embedded System Platform

Authors: Yong-Gyu Lee, Gilwon Yoon

Abstract:

Image processing for capsule endoscopy requires large memory and it takes hours for diagnosis since operation time is normally more than 8 hours. A real-time analysis algorithm of capsule images can be clinically very useful. It can differentiate abnormal tissue from health structure and provide with correlation information among the images. Bleeding is our interest in this regard and we propose a method of detecting frames with potential bleeding in real-time. Our detection algorithm is based on statistical analysis and the shapes of bleeding spots. We tested our algorithm with 30 cases of capsule endoscopy in the digestive track. Results were excellent where a sensitivity of 99% and a specificity of 97% were achieved in detecting the image frames with bleeding spots.

Keywords: bleeding, capsule endoscopy, image processing, real time analysis

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056725

References:

[1] D. G. Adler and C. J. Gostout, "Wireless Capsule Endoscopy", Hospital Physician, 2003, pp.14-22.
[2] A. karargyris and N. Bourbakis, "ASurvey on WCE Imaging Systems and Techniques," IEEE Engineering in Medicine and Biology Magazine, Vol. 29, no. 1, 2010
[3] Intromedic Co., South Korea, www.intromedic.com.
[4] N. Bourbakis, "Detecting abnormal patterns in WCE images," in 5th IEEE Symp. on Bioinformatics and Bioengineering (BIBE-05), Minneapolis, 2005, pp. 26-29.
[5] B. Li and M. Q.-H. Meing, "Analysis of the gastrointestinal status from wireless capsule endoscopy images using local color feature," in Proc. the 2007 Inter. Conf. Information Acquisition, Korea, 2007, pp. 553-557.
[6] J. G. Webster, Design of Pulse Oximeters. Bristol and Philadelphia, CA: Institute of Physics Publishing, 1997, ch. 4.
[7] J. Lee, J. OH,X. Yuan and S.-J. Tang, "Automatic classification of digestive organs in wireless capsule Endoscopy Videos," in Proceedings of the 2007 ACM symposium on Applied computing, 2007, pp. 1041-1045.
[8] P. Y. Lau, and P. L. Correia, "Detection of Bleeding patterns in WCE video using multiple features," in 29th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 5601-5604.
[9] B. Giritharan, X. Yuan, J. Liu, B. Buckles, J. Oh and S. J. Tang, "Bleeding Detection from Capsule Endoscopy Videos," in 30th Annu. IEEE EBMS Conf., Vancouver, 2008, pp. 4780-4783.
[10] A. Karargyris and N. Bourbakis, "A Methodology for Detection Blood-based Abnormalities in Wireless Capsule Endoscopy Videos," in 8th IEEE Inter. Conf. Bioinformatics and Bioengineering, Athens, 2008, pp. 1-6.
[11] Y. S. Jung, Y. H. Kim, D. H. Lee and J. H. Kim, "Active Blood Detection in a High Resolution Capsule Endoscopy using Color Spectrum Transformation," in Int. Conf. BioMedical Engineering and Informatics (BMEI), Sanya, 2008, pp. 859-862.
[12] C. K. Poh, T. M. Htwe, L. Li, W. Shen, j. Liu, J. H. Lim, K. L. Chan, and P. C. Tan, "Multi-Level Local Feature Classification for Bleeding Detection in Wireless Capsule Endoscopy images," in 2010 IEEE conf. on Cybernetics and Intelligent Systems, Singapore , 2010, pp. 76-81.