Skip to main content

Advertisement

SpringerLink
Log in

A saliency map-guided shape compactness for segmentation of polyps in colonoscopy images

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

Abstract

In this research work, a saliency map-based colonoscopy image analysis system is proposed for colonic polyp segmentation. Polyps are the precursor to cancer, and their early diagnosis is very crucial for better prognosis and clinical management. Automated segmentation of such polyps from the highly acquired colonoscopy frames lessens the doctors’ burden and enhances efficiency. In our method, polyps, which are the colonoscopy frames’ salient regions, are detected using a deep convolutional architecture based on U-Net. The shape of the polyps is an essential cue for doctors for its detection and localization. Therefore, an unconstrained shape compactness prior is employed to boost the efficiency of polyp localization. The shape compactness prior, along with the probability map, is formulated into an energy functional, subsequently solved by employing an alternating direction method of multipliers to give the polyp segmentation mask. Our framework can give an average dice similarity coefficient of 82.11% on the CVC-ClinicDB dataset, which is very much competitive to the recent state-of-the-art approaches. Our method thus can be used as a diagnostic tool for colonoscopic image analysis.

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. Arnold, M., Sierra, M.S., Laversanne, M., et al.: Global patterns and trends in colorectal cancer incidence and mortality. Gut 66(4), 683–691 (2016)

    Article  Google Scholar 

  2. Paul, S., Gundabattula, H.D., Seelamantula, C.S., et al.: Fully-automated semantic segmentation of wireless capsule endoscopy abnormalities. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 221–224 (2020)

  3. Silva, J., Histace, A., Romain, O., et al.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)

    Article  Google Scholar 

  4. Cheng, D.C., Ting, W.C., Chen, Y.F., et al.: Colorectal polyps detection using texture features and support vector machine. In: International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology, and Chemistry, pp. 62–72. Springer, (2008)

  5. Iakovidis, D.K., Maroulis, D.E., Karkanis, S.A., et al.: A comparative study of texture features for the discrimination of gastric polyps in endoscopic video. In: Computer-Based Medical Systems, 2005. Proc. 18th IEEE Symposium on, IEEE, pp. 575–580 (2005)

  6. Sasmal, P., Iwahori, Y., Bhuyan, M., et al.: Active contour segmentation of polyps in capsule endoscopic images. In: 2018 International Conference on Signals and Systems (ICSigSys), IEEE, pp. 201–204 (2018)

  7. Sasmal, P., Bhuyan, M., Gupta, S., et al.: Detection of polyps in colonoscopic videos using saliency map-based modified particle filter. IEEE Trans. Instrum. Meas. 70, 1–9 (2021)

    Article  Google Scholar 

  8. Deeba, F., Bui, F.M., Wahid, K.A.: Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomed. Sig. Process. Control 55(101), 530 (2020)

    Google Scholar 

  9. Ding, H., Cen, Q., Si, X., et al.: Automatic glottis segmentation for laryngeal endoscopic images based on u-net. Biomed. Sig. Process. Control 71(103), 116 (2022)

    Google Scholar 

  10. Edwards, J.D., Riley, K.J., Eakins, J.P.: A visual comparison of shape descriptors using multi-dimensional scaling. In: International Conference on Computer Analysis of Images and Patterns, pp. 393–401. Springer, (2003)

  11. Montero, R.S., Bribiesca, E.: State of the art of compactness and circularity measures. In: International mathematical forum, vol. 4, no. 27, pp. 1305–1335 (2009)

  12. Wang, Y., Tavanapong, W., Wong, J., et al.: Part-based multiderivative edge cross-sectional profiles for polyp detection in colonoscopy. IEEE J. Biomed. Health Inform. 18(4), 1379–1389 (2013)

    Article  Google Scholar 

  13. Nawarathna, R., Oh, J., Muthukudage, J., et al.: Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 144, 70–91 (2014)

    Article  Google Scholar 

  14. Rangayyan, R.M., El-Faramawy, N.M., Desautels, J.L., et al.: Measures of acutance and shape for classification of breast tumors. IEEE Trans. Med. Imaging 16(6), 799–810 (1997)

    Article  Google Scholar 

  15. Vázquez, D., Bernal, J., Sánchez, F.J., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017, 9 (2017)

  16. Yu, L., Chen, H., Dou, Q., et al.: Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J. Biomed. Health Inform. 21(1), 65–75 (2016)

    Article  Google Scholar 

  17. Nguyen, Q., Lee, S.W.: Colorectal segmentation using multiple encoder-decoder network in colonoscopy images. In: 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Eengineering (AIKE), pp. 208–211. IEEE, (2018)

  18. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, (2015)

  19. Drozdzal, M., Vorontsov, E., Chartrand, G., et al.: The importance of skip connections in biomedical image segmentation. In: Deep learning and data labeling for medical applications, pp. 179–187. Springer (2016)

  20. Dolz, J., Ayed, I.B., Desrosiers, C.: Unbiased shape compactness for segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 755–763 (2017)

  21. Li, Y., Zhang, K., Shi, W., et al.: Lung fields segmentation based on shape compactness in chest x-ray images. J. Comput. 32(4), 152–165 (2021)

    Google Scholar 

  22. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 9, 1124–1137 (2004)

    Article  Google Scholar 

  23. Zhang, L., Gu, Z., Li, H.: Sdsp: A novel saliency detection method by combining simple priors. In: 2013 IEEE International Conference on Image Processing, IEEE, pp. 171–175 (2013)

  24. Kim, J., Han, D., Tai, Y.W., et al.: Salient region detection via high-dimensional color transform and local spatial support. IEEE Trans. Image Process. 25(1), 9–23 (2015)

    Article  MathSciNet  Google Scholar 

  25. Harel, J., Koch, C., Perona, P.: Graph–Based Visual Saliency. In: Neural information processing systems, vol. 19, pp. 545–552 (2006)

  26. Silva, J.S., Histace, A., Romain, O., Dray, X. Granado, B.: Towards embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)

  27. Bernal, J., Sánchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognit. 45(9), 3166–3182 (2012)

    Article  Google Scholar 

  28. Bernal, J., Núñez, J.M., Sánchez, F.J., et al.: Polyp segmentation method in colonoscopy videos by means of msa-dova energy maps calculation. In: Workshop on Clinical Image-Based Procedures, Springer, pp. 41–49 (2014)

  29. Ganz, M., Yang, X., Slabaugh, G.: Automatic segmentation of polyps in colonoscopic narrow-band imaging data. IEEE Trans. Biomed. Eng. 59(8), 2144–2151 (2012)

    Article  Google Scholar 

  30. Arbelaez, P., Maire, M., Fowlkes, C., et al.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2010)

    Article  Google Scholar 

  31. Banik, D., Roy, K., Bhattacharjee, D., et al.: Polyp-net: a multimodel fusion network for polyp segmentation. IEEE Trans. Instrum. Meas. 70, 1–12 (2020)

    Article  Google Scholar 

  32. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

  33. Feng, S., Zhao, H., Shi, F., et al.: Cpfnet: context pyramid fusion network for medical image segmentation. IEEE Trans. Med. Imaging 39(10), 3008–3018 (2020)

    Article  Google Scholar 

  34. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

  35. Guo, Y.B., Matuszewski, B.: Giana polyp segmentation with fully convolutional dilation neural networks. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, SCITEPRESS-Science and Technology Publications, pp. 632–641 (2019)

  36. Boyd, S., Parikh, N., Chu, E., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)

    MATH  Google Scholar 

  37. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  38. Song, L.M.W.K., Adler, D.G., Conway, J.D., et al.: Narrow band imaging and multiband imaging. Gastrointest. Endosc. 67(4), 581–589 (2008)

    Article  Google Scholar 

  39. Gheorghe, C.: Narrow-band imaging endoscopy for diagnosis of malignant and premalignant gastrointestinal lesions. J. Gastrointest. Liver Dis. 15(1), 77 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of EEE, Indian Institute of Technology, Guwahati, Assam, 781039, India

    Pradipta Sasmal & M. K. Bhuyan

  2. Department of Computer Science, Chubu University, Kasugai, 487-8501, Japan

    Yuji Iwahori

Authors
  1. Pradipta Sasmal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. K. Bhuyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuji Iwahori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pradipta Sasmal.

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

Sasmal, P., Bhuyan, M.K. & Iwahori, Y. A saliency map-guided shape compactness for segmentation of polyps in colonoscopy images. SIViP 16, 2295–2301 (2022). https://doi.org/10.1007/s11760-022-02195-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02195-2

Keywords

Search

Navigation