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A DCP-based Method for Improving Laparoscopic Images

  • Image & Signal Processing
  • Published:
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Abstract

Laparoscopy is an invasive surgical technique performed in abdominal surgery that provides faster recovery than conventional open surgeries. It requires to introduce a camera to observe the surgical maneuvers. However, during this intervention, the quality of the image may be reduced due to the creation of water vapor and carbon dioxide inside the pelvic-abdominal cavity. This phenomenon produces a nebulous image that causes interruptions during the surgical intervention. Removing this nebulous effect is a key factor to improve the vision of the surgeon. In this study, we have used a method based on the dark channel prior to remove the haze in video frames of laparoscopic surgeries to provide better quality images. The results have been positively evaluated by specialists using real video frames of laparoscopic surgeries, thus demonstrating that this method can be effective in improving the quality of the images without losing any detail of the original image.

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References

  1. Schechner Y. Y., Narasimhan S. G., Nayar S. K. (2001) Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. vol. 1, pp I–325–I–332

  2. Shwartz S., Namer E., Schechner Y.Y. (2006) Blind haze separation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2, pp. 1984–1991

  3. Narasimhan S.G., Nayar S.K. (2003) Interactive (De ) Weathering of an Image using Physical Models. In: IEEE Workshop on Color and Photometric Methods in Computer Vision, pp. 1–8

  4. Fattal R.: Single image dehazing.. In: ACM SIGGRAPH 2008 Papers on - SIGGRAPH ’08, vol 27. ACM Press, New York, 2008, p 1

  5. Xu Y., Guo X., Wang H., Zhao F., Peng L. (2016) Single image haze removal using light and dark channel prior. In: 2016 IEEE/CIC International Conference on Communications in China (ICCC). pp. 1–6

  6. Narasimhan S.G., Nayar S.K. (2000) Chromatic framework for vision in bad weather. In: Proceedings. IEEE Conference on Computer Vision and Pattern Recognition, 2000. vol. 1, pp. 598–605. IEEE

  7. Nayar S.K., Narasimhan S.G. (1999) Vision in bad weather. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999. vol. 2, pp. 820–827. IEEE

  8. Kopf J., Neubert B., Chen B., Cohen M., Cohen-Or D., Deussen O., Uyttendaele M., Lischinski D.: Deep photo: Model-based photograph enhancement and viewing. ACM Trans. Graphic. 27 (5): 1, 2008

    Article  Google Scholar 

  9. Zhu Q., Mai J., Shao L., et al.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24 (11): 3522–3533, 2015

    Article  Google Scholar 

  10. Kim T.K., Paik J.K., Kang B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electr. 44 (1): 82–87, 1998

    Article  Google Scholar 

  11. Kim J.-Y., Kim L.-S., Hwang S.-H.: An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circ. Syst. Video Technol. 11 (4): 475–484, 2001

    Article  Google Scholar 

  12. Alex Stark J.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9 (5): 889–896, 2000

    Article  Google Scholar 

  13. Tan R.T. (2008) Visibility in bad weather from a single image. In: 26Th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. pp. 1–8. IEEE

  14. He K., Sun J., Tang X. (2011) Single image haze removal using dark channel prior. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 33, pp 2341–2353

  15. Narasimhan S.G., Nayar S.K. (2003) Contrast restoration of weather degraded images. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 25, pp. 713–724

  16. Ling Z., Fan G., Gong J., Wang Y., Xiao L.: Perception oriented transmission estimation for high quality image dehazing. Neurocomputing 224: 82–95, 2017

    Article  Google Scholar 

  17. Levin A., Lischinski D., Weiss Y. (2008) A closed-form solution to natural image matting. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 30, pp. 228–242

  18. Wang Q., Ward R.K. (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. vol. 53, IEEE

  19. Abdullah-Al-Wadud M., Hasanul Kabir M.d., Ali Akber Dewan M., Chae O.: A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics 53 (2): 593–600, 2007

    Article  Google Scholar 

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Acknowledgements

We are grateful to Dr. Pablo Enríquez Valens for his collaboration in this work.

Funding

This study was supported by the Ministerio de Economía y Competitividad of the Spanish Government (ref. TIN2014-53067-C3-1-R) and co-financed by FEDER.

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Correspondence to Daniel Ruiz-Fernández.

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Ruiz-Fernández, D., Galiana-Merino, J.J., de Ramón-Fernández, A. et al. A DCP-based Method for Improving Laparoscopic Images. J Med Syst 44, 78 (2020). https://doi.org/10.1007/s10916-020-1529-5

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  • DOI: https://doi.org/10.1007/s10916-020-1529-5

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