Bleeding Detection Algorithm for Capsule Endoscopy
Automatic detection of bleeding is of practical
importance since capsule endoscopy produces an extremely large
number of images. Algorithm development of bleeding detection in
the digestive tract is difficult due to different contrasts among the
images, food dregs, secretion and others. In this study, were assigned
weighting factors derived from the independent features of the
contrast and brightness between bleeding and normality. Spectral
analysis based on weighting factors was fast and accurate. Results
were a sensitivity of 87% and a specificity of 90% when the accuracy
was determined for each pixel out of 42 endoscope images.
[1] N. Bourbakis, "Detecting abnormal patterns in WCE images" in 5th IEEE
Symp. on Bioinformatics and Bioengineering (BIBE-05), 2005, pp.
232-328.
[2] Intromedic Corp., South Korea, www.intromedic.com.
[3] A. Glukhovsky, "Wireless capsule endoscopy", Sensor Review, vol. 23,
no. 2, 2003, pp. 128-133.
[4] L. Cui, C. Hu, Y. Zou, and M. Q.-H. Meng, "Bleeding Detection in
Wireless Capsule Endoscopy Images by Support Vector Classifier" in
2010 IEEE Int. Conf. on Information and Automation (ICIA), 2010, pp.
1746-1751.
[5] A. Kararhyris, and N. Bourbakis, "A Methodology for Detecting
Blood-based Abnormalities in Wireless Capsule Endoscopy Videos" in
8th IEEE Int. Conf. on Bioinformatics and Bio Engineering (BIBE-08),
2008, pp. 1-6.
[6] G. Pan, G. Yan, X. Qui, and J. Cui, "Bleeding Detection in Wireless
Capsule Endoscopy Based on Probabilistic Neural Network" Journal of
Medical Systems, vol. 34, Jan. 2010.
[7] P. Y. Lau, and P. L. Correia, "Detection of Bleeding patterns in WCE
video using multiple features" 29th Annual Int. Conf. of the IEEE
Engineering in Medicine and Biology Society, 2007, pp. 5601-5604.
[8] S. A. Prahl., Tabulated molar extinction coefficient for hemoglobin in
water. Oregon Medical Laser Center 2001. Available at:
http://omlc.ogi.edu/spectra/hemoglobin/. Accessed May 13, 2004.
[9] P. Y. Lau, and P. L. Correia, "Analyzing Gastrointestinal Tissue Images
using Multiple Features" in 6th Conf. on Telecommunications, Peniche,
2007, pp. 435-438.
[10] 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, 2010, Singapore, pp. 76-81.
[1] N. Bourbakis, "Detecting abnormal patterns in WCE images" in 5th IEEE
Symp. on Bioinformatics and Bioengineering (BIBE-05), 2005, pp.
232-328.
[2] Intromedic Corp., South Korea, www.intromedic.com.
[3] A. Glukhovsky, "Wireless capsule endoscopy", Sensor Review, vol. 23,
no. 2, 2003, pp. 128-133.
[4] L. Cui, C. Hu, Y. Zou, and M. Q.-H. Meng, "Bleeding Detection in
Wireless Capsule Endoscopy Images by Support Vector Classifier" in
2010 IEEE Int. Conf. on Information and Automation (ICIA), 2010, pp.
1746-1751.
[5] A. Kararhyris, and N. Bourbakis, "A Methodology for Detecting
Blood-based Abnormalities in Wireless Capsule Endoscopy Videos" in
8th IEEE Int. Conf. on Bioinformatics and Bio Engineering (BIBE-08),
2008, pp. 1-6.
[6] G. Pan, G. Yan, X. Qui, and J. Cui, "Bleeding Detection in Wireless
Capsule Endoscopy Based on Probabilistic Neural Network" Journal of
Medical Systems, vol. 34, Jan. 2010.
[7] P. Y. Lau, and P. L. Correia, "Detection of Bleeding patterns in WCE
video using multiple features" 29th Annual Int. Conf. of the IEEE
Engineering in Medicine and Biology Society, 2007, pp. 5601-5604.
[8] S. A. Prahl., Tabulated molar extinction coefficient for hemoglobin in
water. Oregon Medical Laser Center 2001. Available at:
http://omlc.ogi.edu/spectra/hemoglobin/. Accessed May 13, 2004.
[9] P. Y. Lau, and P. L. Correia, "Analyzing Gastrointestinal Tissue Images
using Multiple Features" in 6th Conf. on Telecommunications, Peniche,
2007, pp. 435-438.
[10] 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, 2010, Singapore, pp. 76-81.
@article{"International Journal of Medical, Medicine and Health Sciences:50723", author = "Yong-Gyu Lee and Gilwon Yoon", title = "Bleeding Detection Algorithm for Capsule Endoscopy", abstract = "Automatic detection of bleeding is of practical
importance since capsule endoscopy produces an extremely large
number of images. Algorithm development of bleeding detection in
the digestive tract is difficult due to different contrasts among the
images, food dregs, secretion and others. In this study, were assigned
weighting factors derived from the independent features of the
contrast and brightness between bleeding and normality. Spectral
analysis based on weighting factors was fast and accurate. Results
were a sensitivity of 87% and a specificity of 90% when the accuracy
was determined for each pixel out of 42 endoscope images.", keywords = "bleeding, capsule endoscopy, image analysis,weighted spectrum", volume = "5", number = "9", pages = "391-6", }