Skip to main content
SpringerLink
Log in

Automatic Computer Aided Bleeding Detection Scheme for Wireless Capsule Endoscopy (WCE) Video Based on Higher and Lower Order Statistical Features in a Composite Color

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Wireless capsule endoscopy (WCE) offers noninvasive means to diagnose intestinal anomalies like bleeding. However, one of the problems here is the time consuming complex reviewing process which calls for automatic computer aided bleeding detection techniques to reduce the burden of the physicians. In this paper, pixel based holistic feature extraction scheme is proposed for bleeding frame detection of WCE recordings. Unlike conventional methods, instead of directly using RGB (red, green, blue) color space, a transform color domain is introduced. Higher and lower order statistical analysis on that composite color domain are carried out to extract features from the given WCE image. Feature-based supervised classification using support vector machine is performed to differentiate bleeding and non-bleeding images. Next, in order to improve the bleeding frame detection performance in WCE video, a post-processing scheme is developed utilizing the variation in temporal characteristics of consecutive frames. Finally, a zone detection algorithm is proposed to identify bleeding regions in the detected bleeding images where some morphological operations are also used. Extensive experimentation is carried out on a numerous number of WCE images and videos. It is observed that the proposed algorithm can detect bleeding frame and zones from WCE video recordings with a satisfactory level of performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Adler, D. G., & Gostout, C. J. (2003). Wireless capsule endoscopy. Hospital Physician, 39, 14–22.

    Google Scholar 

  2. Iddan, G., Meron, G., Glukhovsky, A., & Swain, P. (2000). Wireless capsule endoscopy. Nature, 405, 417.

    Article  Google Scholar 

  3. Liu, J., & Yuan, X. (2008). Obscure bleeding detection in endoscopy images using support vector machines. Optimization and Engineering, 10, 289–299.

    Article  MathSciNet  MATH  Google Scholar 

  4. Liangpunsakul, S., Mays, L., & Rex, D. K. (2003). Performance of given suspected blood indicator. Americal Journal of Gastroenterology, 98, 2676–2678.

    Article  Google Scholar 

  5. Li, B., & Meng, M. Q. H. (2009). Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Transactions on Biomedical Engineering, 56, 1032–1039.

    Article  Google Scholar 

  6. Fu, Y., Mandal, M., & Meng, M. Q. H. (2014). Computer aided bleeding detection in WCE video. IEEE Journal of Biomedical and Health Informatics, 18, 636–642.

    Article  Google Scholar 

  7. Guobing, P., Guozheng, Y., Xiangling, Q., & Jiehao, C. (2011). Bleeding detection in wireless capsule endoscopy based on probabilistic neural network. Journal of Medical Systems, 35, 1477–1484.

    Article  Google Scholar 

  8. Sainju, S., Bui, F. M., & Wahid, K. (2013). Bleeding detection in wireless capsule endoscopy based on color features from histogram probability. In Canadian Conference on Electrical and Computer Engineering, 2013, pp. 1–4.

  9. Ghosh, T., Wahid, K., & Fattah, S. A. (2014). Automatic bleeding detection in wireless capsule endoscopy based on RGB pixel intensity ratio. In International Conference on Electrical Engineering and Information Communication Technology, iCEEiCT-2014, pp. 1–4.

  10. Ghosh, T., Bashar, S. K., Alam, M. S., Wahid, K., & Fattah, S. A. (2014). A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images. In International Conference on Informatics Electronics and Vision, ICIEV-2014, pp. 1–4.

  11. Yuan, Y., Li, B., & Meng, M. Q. H. (2016). Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE Journal of Biomedical and Health Informatics, 20(2), 624–630.

    Article  Google Scholar 

  12. Sainju, S., Bui, F. M., & Wahid, K. (2014). Automatic bleeding detection in wireless capsule endoscopy videos using statistical features and region growing. Journal of Medical Systems, 38, 1–11.

    Article  Google Scholar 

  13. Gao, W., Zhang, X., Yang, L., & Liu, H. (2010). An improved sobel edge detection. In 3rd International Conference on Computer Science and Information Technology, ICCSIT, pp. 67–71.

  14. The Capsule Endoscopy Website. (2014). Capsule endoscopy videos. Retrieved from http://www.capsuleendoscopy.org.

  15. Scholkopf, B., & Smola, A. (2002). Learning with Kernels. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  16. Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. British Medical Journal, 308, 1552.

    Article  Google Scholar 

  17. Conversano, F., Franchini, R., Demitri, C., Massoptier, L., Montagna, F., Maffezzoli, A., et al. (2011). Hepatic vessel segmentation for 3D planning of liver surgery: Experimental evaluation of a new fully automatic algorithm. Academic Radiology, 18(4), 461–470.

    Article  Google Scholar 

  18. Conversano, F., Casciaro, E., Franchini, R., Casciaro, S., Lay-Ekuakille, A. (2014). Fully automatic 3D segmentation measurements of human liver vessels from contrast-enhanced CT. In IEEE International Symposium on Medical Measurements and Applications, MeMeA, pp. 1–5.

Download references

Acknowledgements

The authors would like to express their earnest gratefulness towards Dr. M. G. Kibria and Dr. S. N. F. Rumi of Dhaka Medical College and Hospital, Dhaka, Bangladesh, for assisting us to find the bleeding images of a WCE video and delineate the bleeding zones of a bleeding image.

Author information

Authors and Affiliations

  1. Department of EEE, Pabna University of Science and Technology, Pabna, 6600, Bangladesh

    Tonmoy Ghosh

  2. Department of EEE, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

    Shaikh Anowarul Fattah

  3. Department of ECE, University of Saskatchewan, Saskatoon, SK, S7N5A9, Canada

    Khan Arif Wahid

Authors
  1. Tonmoy Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaikh Anowarul Fattah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khan Arif Wahid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaikh Anowarul Fattah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, T., Fattah, S.A. & Wahid, K.A. Automatic Computer Aided Bleeding Detection Scheme for Wireless Capsule Endoscopy (WCE) Video Based on Higher and Lower Order Statistical Features in a Composite Color. J. Med. Biol. Eng. 38, 482–496 (2018). https://doi.org/10.1007/s40846-017-0318-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-017-0318-1

Keywords

Search

Navigation