Skip to main content
SpringerLink
Log in

Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Wireless capsule endoscopy (WCE) is a device that can move through human body and capture the small bowel entirely. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has fuelled the efforts of researchers to provide automated diagnostic techniques for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, a new computer-aided diagnosis method for abnormalities detection in WCE images is proposed. After a preprocessing step, we extract from these images the descriptor we feed to a kernel extreme learning machine to perform the classification process. The descriptor used in this work is a combination between the histogram of oriented gradients (HOG) that were extracted using the hue component of the HSV colour space, and a modified rotation-invariant local binary pattern. The proposed approach has been tested on different datasets, and the results obtained are satisfactory when compared to the state-of-the-art works.

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

Similar content being viewed by others

References

  1. Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph 43, 99–111 (2015)

    Article  Google Scholar 

  2. Charfi, S., El Ansari, M.: A locally based feature descriptor for abnormalities detection. Soft Comput. 24(6), 4469–4481 (2020)

    Article  Google Scholar 

  3. Charisis, V.S., Katsimerou, C., Hadjileontiadis, L.J., Liatsos, C.N., Sergiadis, G.D.: Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: an educational tool to physicians. In: Proceedings of the 26th IEEE International Symposium On Computer-Based Medical Systems, pp. 203–208. IEEE (2013)

  4. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251–1258 (2017)

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 886–893. IEEE (2005)

  6. Deeba, F., Bui, F.M., Wahid, K.A.: Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomed. Signal Process. Control 55, 101530 (2020)

    Article  Google Scholar 

  7. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

  8. El Jaafari, I., El Ansari, M., Koutti, L., Ellahyani, A., Charfi, S.: A novel approach for on-road vehicle detection and tracking. Int. J. Adv. Comput. Sci. Appl. 7(1), 594–601 (2016)

    Google Scholar 

  9. Ellahyani, A., El Ansari, M.: Mean shift and log-polar transform for road sign detection. Multimed. Tools Appl. 76(22), 24495–24513 (2017)

    Article  Google Scholar 

  10. Ellahyani, A., El Ansari, M., El Jaafari, I.: Traffic sign detection and recognition based on random forests. Appl. Soft Comput. 46, 805–815 (2016)

    Article  Google Scholar 

  11. Fan, S., Xu, L., Fan, Y., Wei, K., Li, L.: Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys. Med. Biol. 63(16), 165001 (2018)

    Article  Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  13. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 42(2), 513–529 (2011)

    Article  Google Scholar 

  14. Kopelman, Y., Gal, O., Jacob, H., Siersema, P., Cohen, A., et al.: Automated polyp detection system in colonoscopy using deep learning and image processing techniques. J. Gastroenterol. Compl. 3(1), 101 (2019)

    Google Scholar 

  15. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  16. Öztürk, Ş.: Stacked auto-encoder based tagging with deep features for content-based medical image retrieval. Expert Syst. Appl. 161, 113693 (2020)

    Article  Google Scholar 

  17. Öztürk, Ş., Akdemir, B.: Real-time product quality control system using optimized Gabor filter bank. Int. J. Adv. Manuf. Technol. 96(1–4), 11–19 (2018)

    Article  Google Scholar 

  18. Öztürk, Ş., Bayram, A.: Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images. HELIX 8(3), 3321–3325 (2018)

    Article  Google Scholar 

  19. Park, S., Lee, M., Kwak, N.: Polyp detection in colonoscopy videos using deeply-learned hierarchical features. Seoul National University (2015). https://mipal.snu.ac.kr/images/0/0b/Polyp_short_report.pdf

  20. Qadir, H.A., Balasingham, I., Solhusvik, J., Bergsland, J., Aabakken, L., Shin, Y.: Improving automatic polyp detection using CNN by exploiting temporal dependency in colonoscopy video. IEEE J. Biomed. Health Inf. 24(1), 180–193 (2019)

    Article  Google Scholar 

  21. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Sep. 4, 2014. ArXiv preprint arXiv:1409.1556 (2019)

  22. Souaidi, M., Abdelouahed, A.A., El Ansari, M.: Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images. Multimed. Tools Appl. 78(10), 13091–13108 (2019)

    Article  Google Scholar 

  23. Wittenberg, T., Zobel, P., Rathke, M., Mühldorfer, S.: Computer aided detection of polyps in whitelight-colonoscopy images using deep neural networks. Curr. Dir. Biomed. Eng. 5(1), 231–234 (2019)

    Article  Google Scholar 

  24. Yu, L., Yuen, P.C., Lai, J.: Ulcer detection in wireless capsule endoscopy images. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 45–48. IEEE (2012)

  25. Yuan, Y., Wang, J., Li, B., Meng, M.Q.H.: Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans. Med. Imaging 34(10), 2046–2057 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LabSIE, Department of Mathematics and Computer Science, Multidisciplinary Faculty, Ibn Zohr University, BP 638, 45000, Ouarzazate, Morocco

    Ayoub Ellahyani & Ilyas El Jaafari

  2. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000, Agadir, Morocco

    Said Charfi & Mohamed El Ansari

Authors
  1. Ayoub Ellahyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilyas El Jaafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Said Charfi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed El Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayoub Ellahyani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ellahyani, A., Jaafari, I.E., Charfi, S. et al. Detection of abnormalities in wireless capsule endoscopy based on extreme learning machine. SIViP 15, 877–884 (2021). https://doi.org/10.1007/s11760-020-01809-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01809-x

Keywords

Search

Navigation