Skip to main content
SpringerLink
Log in

Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Modern endoscopes play a significant role in diagnosing various gastrointestinal (GI) tract related diseases where the visual quality of endoscopic images helps improving the diagnosis. This article presents an image enhancement method for color endoscopic images that consists of three stages, and hence termed as “Tri-scan” enhancement: (1) tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics; (2) mucosa layer enhancement: an adaptive sigmoid function is employed on the R plane of the image to highlight micro-vessels of the superficial layers of the mucosa and submucosa; and (3) color tone enhancement: the pixels are uniformly distributed to create an enhanced color effect to highlight the subtle micro-vessels, mucosa and tissue characteristics. The proposed method is used on a large data set of low contrast color white light images (WLI). The results are compared with three existing enhancement techniques: Narrow Band Imaging (NBI), Fuji Intelligent Color Enhancement (FICE) and i-scan Technology. The focus value and color enhancement factor show that the enhancement level achieved in the processed images is higher compared to NBI, FICE and i-scan images.

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
Fig. 13

Similar content being viewed by others

References

  1. Li, B., and Meng, M.Q.-H., Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. J. Vis. Commun. Image Represent. 23(1):222–228, Jan. 2012.

    Article  Google Scholar 

  2. Brownsey, A., and Michalek, J., Wireless capsule endoscopy, Am. Soc. Gastrointest. Endosc., 2010.

  3. Brownsey, A., and Michalek, J., High definition scopes, narrow band imaging, chromoendoscopy, Am. Soc. Gastrointest. Endosc., 2011.

  4. Yousefi-Banaem, H., Rabbani, H., and Adibi, P., Barrett's mucosa segmentation in endoscopic images using a hybrid method: Spatial fuzzy c-mean and level set. J. Med. Signals Sensors. 6(4):231, 2016.

    Google Scholar 

  5. Karkanis, S.A., et al., Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Technol. Biomed. 7(3):141–152, 2003.

    Article  PubMed  Google Scholar 

  6. Deeba, F., et al., Efficacy evaluation of save for the diagnosis of superficial neoplastic lesion. IEEE Journal of Translational Engineering in Health and Medicine (2017). doi:10.1109/JTEHM.2017.2691339.

  7. Sainju, S., Bui, F., and Wahid, K., Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J. Med. Syst. Springer, 38(4):25, 2014.

  8. Machida, H., Sano, Y., Hamamoto, Y., Muto, M., Kozu, T., Tajiri, H., and Yoshida, S., Narrow-band imaging in the diagnosis of colorectal mucosal lesions: A pilot study. Endoscopy. 36:1094–1098, 2004.

    Article  CAS  PubMed  Google Scholar 

  9. Schmitz-valckenberg, S., Holz, F.G., Bird, A.C., and Spaide, R.F., Fundus autofluorescence imaging: Review and perspectives. Retina. 28(3):385–409, 2008.

    Article  PubMed  Google Scholar 

  10. Kaltenbach, T., Sano, Y., Friedland, S., and Soetikno, R., American gastroenterological association (AGA) institute technology assessment on image-enhanced endoscopy. Gastroenterology. 134:327–340, 2008.

    Article  PubMed  Google Scholar 

  11. Kodashima, S., and Fujishiro, M., Novel image-enhanced endoscopy with i-scan technology. World J. Gastroenterol: WJG. 16(9):1043, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  12. PENTAX Medical i-scan Mini- Atlas for Gastroenterology. 2015 [online]. Available: http://www.pentaxmedical.de/media/c770769bdd45ff6481dcd60381870872/PENTAX_i-scan_Mini-Atlas.pdf

  13. Nishimura, J., Nishikawa, J., Nakamura, M., Goto, A., Hamabe, K., Hashimoto, S., and Sakaida, I., Efficacy of i-Scan imaging for the detection and diagnosis of early gastric carcinomas. Gastroenterol. Res. Pract., 2014. doi:10.1155/2014/819395.

  14. Hoffman, A., Loth, L., Rey, J.W., Rahman, F., Goetz, M., Hansen, T., and Kiesslich, R., High definition plus colonoscopy combined with i-scan tone enhancement vs. high definition colonoscopy for colorectal neoplasia: A randomized trial. Dig. Liver Dis. 46(11):991–996, 2014.

    Article  PubMed  Google Scholar 

  15. Pohl, J., May, A., et al., Computed virtual chromoendoscopy: A new tool for enhancing tissue surface structures. Thieme E-Journals - Endoscopy. 39(1):80–83, Jan. 2007.

    CAS  Google Scholar 

  16. Chiu, H., Chang, C., et al., A prospective comparative study of narrow-band imaging, chromoendoscopy, and conventional colonoscopy in the diagnosis of colorectal neoplasia. GUT Int. J. Gastroenterol. Hematol. 56(3):373, 2007.

    Google Scholar 

  17. Curvers, W., et al., Chromoendoscopy and narrow-band imaging compared with high-resolution magnification endoscopy in Barrett’s esophagus. Elsevier - Gastroenterology. 134(3):670–679, 2008.

    Article  Google Scholar 

  18. Chung, S.J., Kim, D., et al., Comparison of detection and miss rates of narrow band imaging, flexible spectral imaging chromoendoscopy and white light at screening colonoscopy: A randomised controlled back-to-back study. Gut. 63:785–791, 2014.

    Article  PubMed  Google Scholar 

  19. Okuhata, H., Nakamura, H., Hara, S., Tsutsui, H. and Onoye T., Application of the real-time Retinex image enhancement for endoscopic images. Proc. Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE, 3407–3410, 2013. doi:10.1109/EMBC.2013.6610273.

  20. Gopi, V.P., and Palanisamy, P., Capsule endoscopic image denoising based on double density dual tree complex wavelet transform. Int. J. Imag. Robot. 9:48–60, 2013.

    Google Scholar 

  21. Agaian, S.S., Silver, B., and Panetta, K.A., Transform coefficient histogram-based image enhancement algorithms using contrast entropy. in IEEE Trans. Image Process. 16(3):741–758, March 2007.

    Article  Google Scholar 

  22. Amini, Z., and Rabbani, H., Statistical modeling of retinal optical coherence tomography. in IEEE Trans. Med. Imaging. 35(6):1544–1554, June 2016.

    Article  Google Scholar 

  23. Imai, F. H. and Berns, R. S., Spectral estimation using trichromatic digital cameras. Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, Chiba University Chiba, Japan, 42:1–8, 1999.

  24. Luft, T., Colditz, C. and Deussen, O., Image enhancement by unsharp masking the depth buffer. ACM, 2006.

  25. Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., and Gualtieri, A., Recent advances in techniques for hyperspectralimage processing. Remote Sens. Environ. 113:S110–S122, 2009.

    Article  Google Scholar 

  26. Hassan, N., and Akamatsu, N., A new approach for contrast enhancement using sigmoid function. The Int. J. of Arab Jour. 1(2):221–225, 2004.

    Google Scholar 

  27. Saruchi, Adaptive sigmoid function to enhance low contrast images. Int. J. of Comp. App. 55(4):45–49, 2012.

    Google Scholar 

  28. Gastrolab – the gastrointestinal site. 1996 [online]. Available: http://www.gastrolab.net/index.htm.

  29. Atlas of Gastrointestinal Endoscopy. 1996 [online]. Available: http://www.endoatlas.com/index.html.

  30. FICE Atlas of Spectral Endoscopic images. 2008 [online]. Available: http://www.fujinonla.com/inc/class/descargar.php?url=/archivos/catalogos/fice-atlas-esp.pdf.

  31. Pizer, S.M., et al., Adaptive histogram equalization and its variations. Comput. Vision, Graphics, Image Process. 39:355–368, 1987.

    Article  Google Scholar 

  32. Wang, Z., and Bovik, A.C., A universal image quality index. Signal Processing Lett., IEEE. 9:81–84, 2002.

    Article  CAS  Google Scholar 

  33. Srivastava, R., Gupta, J.R.P., Parthasarthy, H., and Srivastava, S., PDF based unsharp masking, crispening and high boost filtering of digital images. Commun. Comput. Informat. Sci. 40:8–13, 2009.

    Article  Google Scholar 

  34. Polesel, A., Ramponi, G., and Mathews, V.J., Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9:505–510, 2000.

    Article  CAS  PubMed  Google Scholar 

  35. Xu, X., Wang, Y., Tang, J., Zhang, X., Liu, X., et al. Sensors. 11(9):8281–8294, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Shen, C. H., Chen, H.H., Robust focus measure for low-contrast images, Consumer Electronics, 2006. ICCE'06. 2006 Digest of Technical Papers. International Conference on, IEEE, pp. 69–70, 2006.

  37. Balas, B., Nakano, L., and Rosenholtz, R., A summary-statistic representation in peripheral vision explains visual crowding. J. Vis. 9(12):1–18, Nov. 2009.

    Article  Google Scholar 

  38. Pezzoli, A., et al., Interobserver agreement in describing video capsule endoscopy findings: A multicentre prospective study. Dig. Liver Dis. 43:126–131, 2011.

    Article  PubMed  Google Scholar 

  39. Fawcett, T., An introduction to ROC analysis. Pattern Recogn. Lett. 27:861–874, 2006.

    Article  Google Scholar 

  40. Susstrunk, S. E., and Winkler, S., Color image quality on the internet. Intern. Soc. Opt. Photn., pp. 118–131, 2003.

  41. Iakovidis, D.K., and Koulaouzidis, A., Software for enhanced video capsule endoscopy: Challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3):172–186, 2015.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Grand Challenges Canada (GCC) Star in Global Health, Natural Science and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), and Western Economic Diversification Canada (WED) for their support to this research work. The authors also acknowledge the gastroenterologists who took part in the survey. They are (in no particular order): Dr. Smita Halder from Division of Gastroenterology, Department of Medicine, McMaster University, Dr. Mario Vassallo from Gastroenterologist Department, Mater Dei Hospital, and Dr. Mohammad Amzad Hossain from College of Medicine, University of Saskatchewan.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Saskatchewan, S7N5A9, Saskatoon, SK, Canada

    Mohammad S. Imtiaz, Shahed K. Mohammed, Farah Deeba & Khan A. Wahid

Authors
  1. Mohammad S. Imtiaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahed K. Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farah Deeba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khan A. Wahid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khan A. Wahid.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Imtiaz, M.S., Mohammed, S.K., Deeba, F. et al. Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images. J Med Syst 41, 102 (2017). https://doi.org/10.1007/s10916-017-0738-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0738-z

Keywords

Search

Navigation