Skip to main content
SpringerLink
Log in

A novel method to detect bleeding frame and region in wireless capsule endoscopy video

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

To detect the region and bleeding frame in the wireless capsule endoscopy video, an automatic computer-aided technique is highly demanded to reduce the burden of physicians. The wireless capsule endoscopy (WCE), is an imaging technology which is recently established and doesn’t require any wired device. This device detects abnormalities in GI tract, i.e. (colon, esophagus, small intestine, and stomach). A WCE video consists of 57,000 images. It is very hard to examine by clinicians. To determine bleeding photos out of fifty-seven thousand WCE images makes the task very hard and expensive. The main goal is to develop an automatic obscure bleeding detection method by using superpixel segmentation and naive Bayes classifier. Naive Bayes and superpixel segmentation are used for this problem.

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

Similar content being viewed by others

References

  1. Fu, Y., Zhang, W., Mandal, M., Meng, M.Q.: Computer-aided bleeding detection in WCE video. IEEE J. Biomed. Health Inform. 18(2), 636–42 (2014)

    Article  Google Scholar 

  2. Pan, G., Yan, G., Qiu, X., Cui, J.: Bleeding detection in wireless capsule endoscopy based on probabilistic neural network. J. Med. Syst. 35(6), 1477–1484 (2011)

    Article  Google Scholar 

  3. Al-Rahayfeh, A.A., Abuzneid, A.A.: Detection of bleeding in wireless capsule endoscopy images using range ratio color. Int. J. Multimed. Appl. 2(2), 1–10 (2010)

    Google Scholar 

  4. Hachama, M., Desolneux, A., Richard, F.J.: Bayesian technique for image classifying registration. IEEE Trans. Image Process. 21(9), 4080–4091 (2012)

    Article  MathSciNet  Google Scholar 

  5. Amin, M.M., Kermani, S., Talebi, A., Oghli, M.G.: Recognition of acute lymphoblastic leukemia cells in microscopic images using K-means clustering and support vector machine classifier. J. Med. Signals Sens. 5(1), 49–58 (2015)

    Article  Google Scholar 

  6. Dilna, C., Gopi, V.P.: A novel method for bleeding detection in Wireless Capsule Endoscopic images. In: IEEE International Conference on Computing and Network Communications (CoCoNet), (2015). https://doi.org/10.1109/CoCoNet.2015.7411289

  7. Maghsoudi, O.H., Alizadeh, M., Mirmomen, M.A.: A computer-aided method to detect bleeding, tumor, and disease regions in Wireless Capsule Endoscopy. In: IEEE Conference on Signal Processing in Medicine and Biology Symposium (SPMB), (2016). https://doi.org/10.1109/SPMB.2016.7846852

  8. Yuan, Y., Li, B., Meng, M.Q.: Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J. Biomed. Health Inform. 20(2), 624–630 (2016)

    Article  Google Scholar 

  9. Suman, S., Malik, A.S., Riegler, M., Ho, S.H., Hilmi, I., Goh, K.L.: Detection and classification of bleeding region in WCE images using color feature. In: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, Article No. 17 (2017)

  10. Adam, N., Tachecí, I., Sulík, L., Bureš, J., Krejcar, O.: Automatic blood detection in capsule endoscopy video. J. Biomed. Opt. 21(12), 126007 (2016)

    Article  Google Scholar 

  11. Pan, G.B., Yan, G.Z., Song, X.S., Qiu, X.L.: Bleeding detection from wireless capsule endoscopy images using improved Euler distance in CIELab. J. Shanghai Jiaotong Univ. (Science) 15(2), 218–223 (2010)

    Article  Google Scholar 

  12. Unnimadhavan, R.: Automated bleeding detection in wireless capsule endoscopy videos. J. Biomed. 5(8), 218–224 (2017)

    Google Scholar 

  13. Maghsoudi, O.H., Talebpour, A., Soltanian-Zadeh, H., Alizadeh, M., Soleimani, H.A.: Informative and uninformative regions detection in WCE frames. J. Adv. Comput. 3(1), 12–34 (2014)

    Google Scholar 

  14. Priya, K., Archana, K.S., Neduncheliyanm, S.: Bleeding detection through wireless capsule endoscopy (WCE). Int. J. Adv. Comput. Technol. (IJACT) 4(1), 5–13 (2015)

    Google Scholar 

  15. Hwang, S., Celebi, M.E.: Polyp detection in wireless capsule endoscopy videos based on image segmentation and geometric feature. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), (2010)

  16. Murthi, A., Suganya, D.: Automatic bleeding frame and region detection for GLCM using artificial neural network. J. Adv. Chem. 12(24), 5613–5620 (2016)

    Google Scholar 

  17. Ashok, V., Murugesan, G.: Detection of retinal area from scanning laser ophthalmoscope images (SLO) using deep neural network. Int. J. Biomed. Eng. Technol. 23(2–4), 303–314 (2017)

    Article  Google Scholar 

  18. Coimbra, M., Mackiewicz, M., Fisher, M., Jamieson, C., Soares, J., Silva Cunha, J.P.: Computer vision tools for capsule endoscopy exam analysis. Eurasip NewsLetter 18(1), 1–19 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India

    P. Sivakumar

  2. Department of Computer Science Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India

    B. Muthu Kumar

Authors
  1. P. Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Muthu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Sivakumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivakumar, P., Kumar, B.M. A novel method to detect bleeding frame and region in wireless capsule endoscopy video. Cluster Comput 22 (Suppl 5), 12219–12225 (2019). https://doi.org/10.1007/s10586-017-1584-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1584-y

Keywords

Search

Navigation