216
Biologically Inspired Robotics
11.4 Conclusions
In this chapter we have presented a new scheme that uses color and shape
features to detect intestinal polyps from CE images. The novel features combine the advantages of a chromaticity histogram and Zernike moments in
HSI color space, leading to color invariance and shape invariance. Thus, the
proposed feature shows greater discriminative ability for polyp detection in
CE images compared to a CWC method. Experiments with our present CE
images show that this method is promising for detection of polyp images.
Future work will be directed to collecting more patients’ data in order to test
the robustness of the proposed scheme. Moreover, a suitable quantization
approach for HS histograms is worth further investigation to achieve better
performance.
Acknowledgment
This work was supported by SHIAE project #8115021 of the Shun Hing
Institute of Advanced Engineering of The Chinese University of Hong Kong,
awarded to Max Meng.
References
Adeler, D.G., and Gostout, C.J. 2003. Wireless capsule endoscopy. Hospital Physician,
39(5): 14–22.
Bashar, M.K., Mori, K., Suenaga, Y., Kitasaka, T., and Mekada, Y. 2008. Detecting
informative frames from wireless capsule endoscopic video using color
and texture features. Paper read at the 11th Medical Image Computing and
Computer-Assisted Intervention, New York, September 6–10, 2002.
Bejakovic, S., Kumar, R., Dassopoulos, T., Mullin, G., and Hager, G. 2009. Analysis of
Crohn’s disease lesions in capsule endoscopy images. Paper read at the IEEE
International Conference on Robotics and Automation, Kobe, Japan, May 12–17,
2009.
Berens, J., Mackiewicz, M., and Bell, D. 2005. Stomach, intestine and colon tissue
discriminators for wireless capsule endoscopy images. Proceedings of SPIE on
Medical Imaging, 5747: 283–290.
Chang, C.-C. and Lin, C.-J. 2001. LIBSVM: A library for support vector machines.
Available at http://www.csie.ntu.edu.tw/cjlin/libsvm. Accessed August 18,
2010.
Biologically Inspired Robotics
11.4 Conclusions
In this chapter we have presented a new scheme that uses color and shape
features to detect intestinal polyps from CE images. The novel features combine the advantages of a chromaticity histogram and Zernike moments in
HSI color space, leading to color invariance and shape invariance. Thus, the
proposed feature shows greater discriminative ability for polyp detection in
CE images compared to a CWC method. Experiments with our present CE
images show that this method is promising for detection of polyp images.
Future work will be directed to collecting more patients’ data in order to test
the robustness of the proposed scheme. Moreover, a suitable quantization
approach for HS histograms is worth further investigation to achieve better
performance.
Acknowledgment
This work was supported by SHIAE project #8115021 of the Shun Hing
Institute of Advanced Engineering of The Chinese University of Hong Kong,
awarded to Max Meng.
References
Adeler, D.G., and Gostout, C.J. 2003. Wireless capsule endoscopy. Hospital Physician,
39(5): 14–22.
Bashar, M.K., Mori, K., Suenaga, Y., Kitasaka, T., and Mekada, Y. 2008. Detecting
informative frames from wireless capsule endoscopic video using color
and texture features. Paper read at the 11th Medical Image Computing and
Computer-Assisted Intervention, New York, September 6–10, 2002.
Bejakovic, S., Kumar, R., Dassopoulos, T., Mullin, G., and Hager, G. 2009. Analysis of
Crohn’s disease lesions in capsule endoscopy images. Paper read at the IEEE
International Conference on Robotics and Automation, Kobe, Japan, May 12–17,
2009.
Berens, J., Mackiewicz, M., and Bell, D. 2005. Stomach, intestine and colon tissue
discriminators for wireless capsule endoscopy images. Proceedings of SPIE on
Medical Imaging, 5747: 283–290.
Chang, C.-C. and Lin, C.-J. 2001. LIBSVM: A library for support vector machines.
Available at http://www.csie.ntu.edu.tw/cjlin/libsvm. Accessed August 18,
2010.