endoscopes (WCEs) have been used widely to detect abnormalities inside regions
of the small intestine that are not accessible when using traditional endoscopy
techniques. However, an experienced clinician must spend an average of 2 hours
to view and analyze the approximately 60,000 images produced during one
examination. Therefore, developing a computeraided system for processing WCE
images is crucial. This paper proposes a novel method used for detecting
bleeding and ulcers in WCE images. This approach involves using color features
to determine the status of the small intestine. The experimental results
revealed that the proposed scheme is promising in detecting bleeding and ulcer
Cite this paper
Yeh, J. , Wu, T. and Tsai, W. (2014) Bleeding and Ulcer Detection Using Wireless Capsule Endoscopy Images. Journal of Software Engineering and Applications
, 422-432. doi: 10.4236/jsea.2014.75039
 Iddan, G., Meron, G., Glukhovsky, A. and Swain, P. (2000) Wireless Capsule Endoscopy. Nature, 405, 725-729.
 Li, B.P. and Meng, M.Q.-H. (2009) Computer-Based Detection of Bleeding and Ulcer in Wireless Capsule Endoscopy Images by Chromaticity Moments. Computers in Biology and Medicine, 39, 141-147. http://dx.doi.org/10.1016/j.compbiomed.2008.11.007
 Adeler, D.G. and Gostout, C.J. (2003) Wireless Capsule Endoscopy. Hospital Physician, 39, 14-22.
 Kodogiannis, V.S., Boulougoura, M., Wadge, J.N. and Lygouras, J.N. (2007) The Usage of Soft-Computing Methodologies in Interpreting Capsule Endoscopy. Engineering Applications of Artificial Intelligence, 20, 539-553.
 Li, B.P. and Meng, M.Q.-H. (2009) Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images. IEEE Transactions on Biomedical Engineering, 56, 1032-1039. http://dx.doi.org/10.1109/TBME.2008.2010526
 Karargyris, A. and Bourbakis, N. (2010) Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented. IEEE Engineering in Medicine and Biology Magazine, 29, 72-83.
 Miaou, S.-G., Chang, F.-L., Timotius, I.K., Huang, H.-C., Su J.-L., Liao, R.-S. and Lin, T.-Y. (2009) A Multi-Stage Recognition System to Detect Different Types of Abnormality in Capsule Endoscope Images. Journal of Medical and Biological Engineering, 29, 114-121.
 Smith, A.R. (1978) Color Gamut Transform Pairs. Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '78), New York, 23-25 August 1978, 12-19.
 Pass, G., Zabih, R. and Miller, J. (1996) Comparing Images Using Color Coherence Vectors. Proceedings of the 4th ACM International Conference on Multimedia, Boston, 18-22 November 1996, 65-73. http://dx.doi.org/10.1145/244130.244148
 Witten, I.H. and Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition, Morgan Kaufmann, Burlington.
 Kononenko, I. (1994) Estimation Atributes: Analysis and Extensions of RELIEF. Proceedings of the 1994 European Conference on Machine Learning, Catania, 6-8 April 1994, 171-182.
 Ubeyli, E.D. (2007) Comparison of Different Classification Algorithms in Clinical Decision-Making. Expert Systems, 24, 17-31. http://dx.doi.org/10.1111/j.1468-0394.2007.00418.x
 Guyon, I., Weston, J., Barnhill S. and Vapnik, V. (2002) Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning, 46, 389-422. http://dx.doi.org/10.1023/A:1012487302797
 Han, J. and Kamber, M. (2011) Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann, Burlington.