Empirical Comparison of Visual Descriptors for Multiple Bleeding Spots Recognition in Wireless Capsule Endoscopy Video

  • Sarah Alotaibi
  • Sahar Qasim
  • Ouiem Bchir
  • Mohamed Maher Ben Ismail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)

Abstract

Wireless Capsule Endoscopy (WCE) is the latest technology able to screen intestinal anomalies at early stage. Although its convenience to the patient and its effectiveness to show small intestinal details, the physician diagnosis remains not straight forward and time consuming. Thus, a computer aid diagnosis would be helpful. In this paper, we focus on The Multiple Bleeding Spots (MBS) anomaly. We propose to conduct an empirical evaluation of four feature descriptors in a the challenging problem of MBS recognition on WCE video using the SVM classifier. The performance of the four descriptors is based on the assessment of the performance of the output of the SVM classifier.

Keywords

Wireless Capsule Endoscopy Feature descriptors SVM Multiple Bleeding Spots 

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References

  1. 1.
    Gerber, J., Bergwerk, A., Fleischer, D.: Gastrointestinal Endoscopy, vol. 66, pp. 1188–1195 (2007)Google Scholar
  2. 2.
    Li, B., Meng, M.-H.: Wireless capsule endoscopy images enhancement using contrast driven forward and backward anisotropic diffusion. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 2, pp. II–437 (2007)Google Scholar
  3. 3.
    Miaou, S., Chang, F., Timotius, I., Huang, H.: A Multi-stage Recognition System to Detect Different Types of Abnormality in Capsule Endoscope Images. Journal of Medical and Biological Engineering 29, 114–121 (2009)Google Scholar
  4. 4.
    Mewes, P.W., Rennert, P., Juloski, A.L., Lalande, A., Angelopoulou, E., Kuth, R., Hornegger, J.: Semantic and topological classification of images in magnetically guided capsule endoscopy. In: SPIE Medical Imaging, p. 83151A (2012)Google Scholar
  5. 5.
    Kodogiannis, V., Lygouras, J.N.: Neuro-fuzzy classification system for wireless-capsule endoscopic images. Int. J. Electr. Comput. Syst. Eng. 2, 55–63 (2008)Google Scholar
  6. 6.
    Bashar, M., Kitasaka, T., Suenaga, Y., Mekada, Y., Mori, K.: Automatic Detection of Informative Frames from Wireless Capsule Endoscopy Images. In: Medical Image Analysis, vol. 14, pp. 449–470 (2010)Google Scholar
  7. 7.
    Suykens, J., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. In: Neural Processing Letters, 9th edn., pp. 293–300 (1999)Google Scholar
  8. 8.
    Khun, P.C., Zhuo, Z., Yang, L.Z., Liyuan, L., Jiang, L.: Feature selection and classification for wireless capsule endoscopic frames. In: International Conference on Biomedical and Pharmaceutical Engineering, ICBPE 2009, pp. 1–6 (2009)Google Scholar
  9. 9.
    References, B., Li, M.: Capsule endoscopy images classification by color texture and support vector machine. In: 2010 IEEE International Conference on Automation and Logistics (ICAL), pp. 126–131 (2010)Google Scholar
  10. 10.
    References, L., Yu, P., Yuen, J.: Ulcer detection in wireless capsule endoscopy images. In: ICPR, pp. 45–48 (2012)Google Scholar
  11. 11.
    References, Y.-G.: Time Image Analysis of Capsule Endoscopy for Bleeding Discrimination in Embedded System Platform. International Journal of Biological and Life Sciences, World Academy of Science, Engineering and Technology 60, 1030–1034 (2011)Google Scholar
  12. 12.
    Stricker, M., Orengo, M.: Similarity of color image. In: SPIE Conference on Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 381–392 (February 1995)Google Scholar
  13. 13.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)MATHCrossRefGoogle Scholar
  14. 14.
    Jain, A.K., Ratha, N.K., Lakshmanan, S.: Object detection using Gabor filters. Pattern Recognition 30(2), 295–309 (1997)CrossRefGoogle Scholar
  15. 15.
    Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG 7: Multimedia content description language. John Wiley (2002)Google Scholar
  16. 16.
    Cortes, C., Vapnik, V.N.: Support-Vector Networks. Machine Learning 20 (1995)Google Scholar
  17. 17.
    Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing for Computer Vision, 3rd edn (2012)Google Scholar
  18. 18.
    Coimbra, M.T., Cunha, J.P.S.: MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy. Circuits and Systems for Video Technology 16, 628–637 (2006)CrossRefGoogle Scholar
  19. 19.
    Baopu, L., Meng, M.Q.H.: Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection. Information Technology in Biomedicine 16, 323–329 (2012)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Frigui, H., Nasraoui, O.: Unsupervised learning of prototypes and attribute weights. Pattern Recognition 37, 567–581 (2004)CrossRefGoogle Scholar
  22. 22.
    Swets, J.A.: Signal detection theory and ROC analysis in psychology and diagnostics: collected papers. Lawrence Erlbaum Associates, Mahwah (1996)MATHGoogle Scholar
  23. 23.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction, 2nd edn (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sarah Alotaibi
    • 1
  • Sahar Qasim
    • 1
  • Ouiem Bchir
    • 1
  • Mohamed Maher Ben Ismail
    • 1
  1. 1.College of Computer and Information SciencesKing Saud UniversitySaudi Arabia

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