Abstract
Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are provided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learning framework in order to obtain the characterization of diverse motility events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sonka, M., Fitzpatrick, J.M.: Handbook of Medical Imaging. SPIE Press (2000)
Iddan, G., Meron, G., et al.: Wireless capsule endoscopy. Nature 405, 417 (2000)
Tjoa, M.P., Krishnan, S.M.: Feature extraction for the analysis of colon status from the endoscopic images. Biomedical Engineering OnLine 2, 3–17 (2003)
Karkanis, S.A., Iakovidis, D.K., et al.: Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Transactions on Information Technology in Biomedicine 7, 141–152 (2003)
Magoulas, G., Plagianakos, V., et al.: Neural network-based colonoscopic diagnosis using online learning and differential evolution. Applied Soft Computing 4, 369–379 (2004)
Zheng, M.M., Krishnan, S.M., Tjoa, P.: A fusion-based clinical support for disease diagnosis from endoscopic images. Computers in Biology and Medicine 35(3), 259–274 (2005)
Kodogiannis, V.S., Chowdrey, H.S.: Multi-network classification scheme for computer-aided diagnosis in clinical endoscopy. In: Proceedings of the International Conference on Medical Signal Processing (MEDISP), pp. 262–267 (2004)
Boulougoura, M., Wadge, V., et al.: Intelligent systems for computer-assisted clinical endoscopic image analysis. In: Proceedings of the 2nd IASTED Conference on Biomedical Engineering Innsbruck, pp. 405–408 (2005)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks, 988–999 (1999)
Tipping, M.: The relevance vector machine. In: Advances in Neural Information Processing Systems, San Mateo, CA, Morgan Kaufmann, San Francisco (2000)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
CIE: Colorimetry - part 4: Cie 1976 l*a*b* colour spaces. Cie draft standard ds 014-4.2/e:2006 (2006)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)
Russ, J.C.: The Image Processing Handbook. CRC Press, Boca Raton (1999)
Vilariño, F., Spyridonos, P., Vitrià, J., de Iorio, F., Azpiroz, F., Radeva, P.: Intestinal motility assessment with video capsule endoscopy: Automatic annotation of intestinal contractions. IEEE Trans. on Medical Imaging (under revision) (2006)
Spyridonos, P., Vilariño, F., Vitrià, J., Radeva, P.: Anisotropic feature extraction from endoluminal images for detection of intestinal contractions. LNCS (in press, 2006)
Given Imaging, L. (2007), http://www.givenimaging.com
Stone, M.: Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society B 36, 111–147 (1974)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Seguí, S. et al. (2008). Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-79547-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79546-9
Online ISBN: 978-3-540-79547-6
eBook Packages: Computer ScienceComputer Science (R0)