SpringerLink
Log in

A comprehensive study on color correction for medical facial images

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The color images produced by digital cameras are usually not in conformity with their intrinsic colors. This will seriously affect computer-aided medical facial image analysis because it is on the basis of accurate rendering of color information. To solve that, we propose an optimized color correction scheme for medical facial images based on a comprehensive study in various aspects: color spaces, color patches and color correction algorithms. Firstly, we utilize undistorted facial images to demarcate complexion gamut. Secondly, we choose the whole color patches located inside the range of complexion gamut as our validation samples and select the most crucial color patches from the gamut as our optimized training samples, followed by the optimization criteria. Thirdly, we select an adaptive target device-independent color space for medical facial images color correction task. Finally, we evaluate the performance of three most popular color correction algorithms, and select the most suitable one to build our final regression model. Qualitative and quantitative experimental results show that the proposed scheme is superior to that based on the ColorChecker 24 and performs very closely to the benchmark training error results while only 24 colors are involved. More importantly, the acquisition environment is well-designed with the system color chromatic aberration of 5.3369, which is quite small and hard to improve, but the color difference still can be perceived by human vision. Through our optimized color correction scheme, we succeed in reducing the color chromatic aberration to 1.5898. Furthermore, the corrected facial images are more consistent with the observed colors by human beings. Compared with the previous works, our color correction scheme is characterized by mission dependence and statistical reliability. Besides, the optimized color correction model has low complexity and high accuracy. All of these features make this scheme distinctive from previous color correction methods and effective for medical facial images color correction task.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  2. Cheung V, Westland S, Connah D, Ripamonti C (2004) A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms. Color Technol 120(1):19–25

    Article  Google Scholar 

  3. Connolly C, Fleiss T (1997) A study of efficiency and accuracy in the transformation from rgb to cielab color space. Image Process IEEE Trans 6(7):1046–1048

    Article  Google Scholar 

  4. Fasel I, Fortenberry B, Movellan J (2005) A generative framework for real time object detection and classification. Comput Vis Image Underst 98(1):182–210

    Article  Google Scholar 

  5. Fink B, Bunse L, Matts P, D’Emiliano D (2012) Visible skin colouration predicts perception of male facial age, health and attractiveness. Int J Cosmet Sci 34(4):307–310

    Article  Google Scholar 

  6. Fink B, Grammer K, Matts PJ (2006) Visible skin color distribution plays a role in the perception of age, attractiveness, and health in female faces. Evol Hum Behav 27(6):433–442

    Article  Google Scholar 

  7. Gijsenij A, Gevers T, Van De Weijer J (2011) Computational color constancy: survey and experiments. Image Process IEEE Trans 20(9):2475–2489

    Article  MathSciNet  MATH  Google Scholar 

  8. Hong G, Luo MR, Rhodes PA (2015) A study of digital camera colorimetric characterisation based on polynomial modelling. Color Res Appl 26(1):76–84

    Article  Google Scholar 

  9. Li F, Zhao C, **a Z, Wang Y, Zhou X, Li GZ (2012) Computer-assisted lip diagnosis on traditional chinese medicine using multi-class support vector machines. BMC Complement Altern Med 12(1):127

    Article  Google Scholar 

  10. Li X, Li F, Wang Y, Qian P, Zheng X (2009) Computer-aided disease diagnosis system in tcm based on facial image analysis. Int J Funct Inf Personal Med 2(3):303–314

    Google Scholar 

  11. Liu Y, Yu H, Shi J (2008) Camera characterization using back-propagation artificial neutral network based on munsell system. In: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, pp 66,210A–66,210A. International Society for Optics and Photonics

  12. Lo LC, Cheng TL, Chen YJ, Natsagdorj S, Chiang JY (2014) Traditional chinese medicine tongue diagnosis index of early-stage breast cancer. In: Medical Biometrics (ICMB), 2014 International Conference on, pp 7–14. IEEE

  13. Matts PJ, Fink B, Grammer K, Burquest M (2007) Color homogeneity and visual perception of age, health, and attractiveness of female facial skin. J Am Acad Dermatol 57(6):977–984

    Article  Google Scholar 

  14. Ohta N, Robertson A (2005) Colorimetry: fundamentals and applications. Wiley, Chichester. https://doi.org/10.1002/0470094745

    Book  Google Scholar 

  15. Penczek J, Boynton PA, Splett JD (2014) Color error in the digital camera image capture process. J Digit Imaging 27(2):182–191

    Article  Google Scholar 

  16. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  17. Specht DF (1991) A general regression neural network. Neural Netw IEEE Trans 2(6):568–576

    Article  Google Scholar 

  18. Stephen ID, Coetzee V, Perrett DI (2011) Carotenoid and melanin pigment coloration affect perceived human health. Evol Hum Behav 32(3):216–227

    Article  Google Scholar 

  19. Stephen ID, Oldham FH, Perrett DI, Barton RA (2012) Redness enhances perceived aggression, dominance and attractiveness in men’s faces. Evol Psychol 10(3):562–572

    Article  Google Scholar 

  20. Stephen ID, Smith MJL, Stirrat MR, Perrett DI (2009) Facial skin coloration affects perceived health of human faces. Int J Primatol 30(6):845–857

    Article  Google Scholar 

  21. Tan WR, Chan CS, Yogarajah P, Condell J (2012) A fusion approach for efficient human skin detection. Ind Inf IEEE Trans 8(1):138–147

    Article  Google Scholar 

  22. Tkalcic M, Tasic JF et al (2003) Colour spaces: perceptual, historical and applicational background. In: Eurocon, pp 304–308

  23. Wang X, Zhang B, Guo Z, Zhang D (2013) Facial image medical analysis system using quantitative chromatic feature. Expert Syst Appl 40(9):3738–3746

    Article  Google Scholar 

  24. Wang X, Zhang D (2010) A comparative study of color correction algorithms for tongue image inspection. In: Medical Biometrics, pp 392–402. Springer

  25. Wang X, Zhang D (2010) An optimized tongue image color correction scheme. Inf Technol Biomed IEEE Trans 14(6):1355–1364

    Article  Google Scholar 

  26. Wang X, Zhang D (2013) A high quality color imaging system for computerized tongue image analysis. Expert Syst Appl 40(15):5854–5866

    Article  Google Scholar 

  27. Zhang HZ, Wang KQ, ** XS, Zhang D (2005) Svr based color calibration for tongue image. In: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, vol 8, pp 5065–5070. IEEE

Download references

Acknowledgements

This research was supported by the Natural Science Foundation of China under Grant No. 61273305 and supported by the Fundamental Research Funds for the Central public welfare research institutes No. ZZ0908032.

Author information

Authors and Affiliations

  1. Department of Control Science, Tongji University, Shanghai, China

    **Ling Niu, ChangBo Zhao & Guo-Zheng Li

  2. China Academy of Chinese Medical Science, Bei**g, China

    Guo-Zheng Li

Authors
  1. **Ling Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ChangBo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guo-Zheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guo-Zheng Li.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niu, J., Zhao, C. & Li, GZ. A comprehensive study on color correction for medical facial images. Int. J. Mach. Learn. & Cyber. 10, 935–947 (2019). https://doi.org/10.1007/s13042-017-0773-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-017-0773-6

Keywords

Search

Navigation