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Automatic Region-of-Interest Segmentation and Pathology Detection in Magnetically Guided Capsule Endoscopy

  • Philip W. Mewes
  • Dominik Neumann
  • Oleg Licegevic
  • Johannes Simon
  • Aleksandar Lj. Juloski
  • Elli Angelopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operator’s ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.

Keywords

Capsule Endoscopy Image Patch Capsule Endoscopy Video Wireless Capsule Endoscopy Intestinal Juice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Berens, J., Mackiewicz, M., Bell, D.: Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images. In: Fitzpatrick, J., Reinhardt, J. (eds.) Proceedings of SPIE, vol. 5747, p. 283 (2005)Google Scholar
  2. 2.
    Cunha, J., Coimbra, M., Campos, P., Soares, J.: Automated topographic segmentation and transit time estimation in endoscopic capsule exams. IEEE T. Med. Imaging 27(1), 19–27 (2007)CrossRefGoogle Scholar
  3. 3.
    Hu, M.: Visual pattern recognition by moment invariants. IRE Trans. Info. Theory 8, 179–187 (1962)zbMATHGoogle Scholar
  4. 4.
    Karargyris, A., et al.: A video-frame based registration using segmentation and graph connectivity for Wireless Capsule Endoscopy. In: Life Science Systems and Applications Workshop LiSSA 2009, IEEE/NIH, pp. 74–79. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  5. 5.
    Mackiewicz, M., Berens, J., Fisher, M.: Wireless capsule endoscopy color video segmentation. IEEE T. Med. Imaging 27(12), 1769–1781 (2008)CrossRefGoogle Scholar
  6. 6.
    Mackiewicz, M., Fisher, M., Jamieson, C.: Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification. In: Joseph, M., Josien, P. (eds.) Proceedings of SPIE, vol. 6914, p. 69140R (2008)Google Scholar
  7. 7.
    Menciassi, A., Valdastri, P., Quaglia, C., Buselli, E., Dario, P.: Wireless steering mechanism with magnetic actuation for an endoscopic capsule. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 1204–1207. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  8. 8.
    Mewes, P., Neumann, D., Juloski, A., Angelopoulou, E., Hornegger, J.: On-the-fly detection of images with gastritis aspects in magnetically-guided capsule endoscopy. In: Ronald, M., van Ginneken, B. (eds.) Medical Imaging - Computer-Aided Diagnosis. Proceedings of the SPIE, vol. 7963, p. 79631I3. SPIE, San Jose (2011)Google Scholar
  9. 9.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  10. 10.
    Reingruber, H.: Intestinal content detection in capsule endoscopy using robust features @ONLINE (2009), http://www.recercat.net/handle/2072/43221
  11. 11.
    Rey, J., Ogata, H., Hosoe, N., Ohtsuka, K., Ogata, N., Ikeda, K., Aihara, H., Pangtay, I., Hibi, T., Kudo, S., Tajiri, H.: Feasibility of stomach exploration with a guided capsule endoscope. Endoscopy 42(7), 541–545 (2010)CrossRefGoogle Scholar
  12. 12.
    Swain, P., Toor, A., Volke, F., Keller, J., Gerber, J., Rabinovitz, E., Rothstein, R.: Remote magnetic manipulation of a wireless capsule endoscope in the esophagus and stomach of humans. Gastrointest Endosc (2010)Google Scholar
  13. 13.
    Szczypinski, P., Klepaczko, A.: Selecting texture discriminative descriptors of capsule endpscopy images. In: Zinterhof, P. (ed.) Proceedings of ISPA 2009, pp. 701–706. IEEE, Los Alamitos (2009)Google Scholar
  14. 14.
    Szczypiski, P., Sriram, P., Sriram, R., Reddy, D.: Model of deformable rings for aiding the wireless capsule endoscopy video interpretation and reporting. Comp. Imag. Vis., 167–172 (2006)Google Scholar
  15. 15.
    Vilarino, F., Spyridonos, P., Pujol, O., Vitria, J., Radeva, P., De Iorio, F.: Automatic detection of intestinal juices in wireless capsule video endoscopy. In: Tang, Y., Wang, S., Lorette, G., Yeung, D., Yan, H. (eds.) ICPR 2006, vol. 4, pp. 719–722. IEEE, Los Alamitos (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Philip W. Mewes
    • 1
    • 2
  • Dominik Neumann
    • 1
  • Oleg Licegevic
    • 1
  • Johannes Simon
    • 1
  • Aleksandar Lj. Juloski
    • 1
  • Elli Angelopoulou
    • 2
  1. 1.Healthcare SectorSiemens AGErlangenGermany
  2. 2.Pattern Recognition LabUniversity of Erlangen-NürnbergErlangenGermany

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