Skip to main content
SpringerLink
Log in

A multi-class bi-level thresholding method for accurate anthropometric measurements of scanned plantar images

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

Abstract

Traditionally, the anthropometric measurements of foot or insole were taken manually by an anthropometrist for clinical and sporty purposes. Digital foot scanners have been developed in recent years to yield anthropometrists digital image of plantar with pressure distribution and anthropometric information. In this paper, an automated thresholding method based on Shanbag entropy and the weighted gray level spatial correlation histogram is presented for analysis of scanned foot images. The presented method compared with traditional two-dimensional histogram and other single-class-based methods automatically accurately detects the foot edges and areas. Also, by background removal, it attains a perfect foot image while in which white pixels of the resulting thresholded image correspond to the points of the scanner which detects pressure and on the contrary the black pixels correspond to background. We are taking into accounts the image’s local properties along with its global properties in a fuzzy domain by employing weighted gray level spatial correlation histogram (W-GLSC). First, a three-dimensional histogram based on the statistics of the gray levels’ probability and similarity with neighboring pixels (GLSC histogram) is weighted by the membership values assigned by Jawahar clustering method for foreground–background discrimination. Then, the Shanbag entropy is used to maximize the information transfer from the original image to the resulting thresholded image. Resulting binary images are undergone anthropometric measurements by considering the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper.

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

Similar content being viewed by others

References

  1. Alexander, I., Chao, E., Johnston, K.: The assessment of dynamic foot-to-ground contact forces and plantar pressure distribution: a review of the evolution of current techniques and clinical applications. Foot Ankle 11, 152–167 (1990)

    Article  Google Scholar 

  2. Cavanagh, P.R., Ulbrecht, J.S., Caputo, G.M.: Keynote lecture: from laboratory to clinic: where can plantar pressure measurement make a contribution? In: Conference Proceedings: V Emed Scientific Meeting, 17–20. August Pennsylvania State University (1996)

  3. Tsai, M., Chen, Y.H.: A fast histogram-clustering approach for multilevel thresholding. Pattern Recognit. Lett. 13(4), 245–252 (1992)

    Google Scholar 

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

    MathSciNet  Google Scholar 

  5. Tsai, W.H.: Moment-preserving thresholding: a new approach. Comput. Vis. Graph. Image Process. 29, 377–393 (1985)

    Article  Google Scholar 

  6. Yen, J.C., Chang, F.J., Chang, S.: A new criterion for automatic multilevel thresholding. IEEE Trans. Image Process. 4(3), 370–378 (1995)

    Article  Google Scholar 

  7. Wang, S., Haralick, R.: Automatic multi threshold selection. Comput. Vis. Graph. Image Process. 25, 46–67 (1984)

    Article  Google Scholar 

  8. Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41, 233–260 (1988)

    Article  Google Scholar 

  9. Pun, T.: A new method for gray level picture thresholding using the entropy of the histogram. Signal Process. 2, 223–237 (1980)

    Article  Google Scholar 

  10. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)

    Article  Google Scholar 

  11. Lee, S.U., Chung, S.Y.: A comparative performance study of several global thresholding techniques for segmentation. Comput. Vis. Graph. Image Process. 52, 171–190 (1990)

    Article  Google Scholar 

  12. Abutaleb, S.: Automatic thresholding of gray level pictures using two-entropy. Comput. Vis. Graph. Image Process. 47, 22–32 (1989)

    Article  Google Scholar 

  13. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J Electron. imaging 13(1), 146–165 (2004)

    Article  Google Scholar 

  14. Weszka, J.S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Trans. Syst. Man Cybern. SMC–8, 627–629 (1978)

    Google Scholar 

  15. Rosenfeld, A., De la Torre, P.: Histogram concavity analysis as an aid in threshold selection. IEEE Trans. Syst. Man Cybern. SMC–13, 231–235 (1983)

    Article  Google Scholar 

  16. Weszka, J., Rosenfeld, A.: Histogram modification for threshold selection. IEEE Trans. Syst. Man Cybern. SMC–9, 38–52 (1979)

    Google Scholar 

  17. Halada, L., Osokov, G.A.: Histogram concavity analysis by quasicurvature. Comput. Artif. Intell. 6, 523–533 (1987)

    Google Scholar 

  18. Sahasrabudhe, S.C., Gupta, K.S.D.: A valley-seeking threshold selection technique. Comput. Vis. Image Underst. 56, 55–65 (1992)

    Google Scholar 

  19. Guo, R., Pandit, S.M.: Automatic threshold selection based on histogram modes and a discriminant criterion. Mach. Vis. Appl. 10, 331–338 (1998)

    Article  Google Scholar 

  20. Cai, J., Liu, Z.Q.: A new thresholding algorithm based on all-pole model. In: ICPR’98, International Conference Pattern Recognition, pp. 34–36 (1998)

  21. Ramesh, N., Yoo, J.H., Sethi, I.K.: Thresholding based on histogram approximation. IEE Proc. Vis. Image Signal Process. 142(5), 271–279 (1995)

    Google Scholar 

  22. Kampke, T., Kober, R.: Nonparametric optimal binarization. In: ICPR’98, International Conference Pattern Recognition, pp. 27–29 (1998)

  23. Sezan, M.I.: A peak detection algorithm and its application to histogram-based image data reduction. Graph. Models Image Process. 29, 47–59 (1985)

    Article  Google Scholar 

  24. Carlotto, M.J.: Histogram analysis using a scale-space approach. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–9, 121–129 (1997)

    Google Scholar 

  25. Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. SMC–8, 630–632 (1978)

    Google Scholar 

  26. Leung, C.K., Lam, F.K.: Performance analysis of a class of iterative image thresholding algorithms. Pattern Recognit. 29(9), 1523–1530 (1996)

    Article  Google Scholar 

  27. Trussel, H.J.: Comments on picture thresholding using iterative selection method. IEEE Trans. Syst. Man Cybern. SMC–9, 311 (1979)

    Article  Google Scholar 

  28. Yanni, M.K., Horne, E.: A new approach to dynamic thresholding. In: EUSIPCO’94: 9th European Conference Signal Processing, vol. 1, pp. 34–44 (1994)

  29. Lloyd, E.: Automatic Target Classification Using Moment Invariant of Image Shapes. Technical Report, RAE IDN AW126, Farnborough, UK (1985)

  30. Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986)

    Article  Google Scholar 

  31. Cho, S., Haralick, R., Yi, S.: Improvement of Kittler and Illingworths’s minimum error thresholding. Pattern Recognit. 22, 609–617 (1989)

    Article  Google Scholar 

  32. Kittler, J., Illingworth, J.: On threshold selection using clustering criteria. IEEE Trans. Syst. Man Cybern. SMC–15, 652–655 (1985)

    Article  Google Scholar 

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

    Google Scholar 

  34. Jawahar, C.V., Biswas, P.K., Ray, A.K.: Investigations on fuzzy thresholding based on fuzzy clustering. Pattern Recognit. 30(10), 1605–1613 (1997)

    Article  MATH  Google Scholar 

  35. Xiao, Y., Cao, Z., Zhang, T.: Entropic thresholding based on gray level spatial correlation histogram, 978–1-4244-2175-6. In: IEEE, 08

  36. Pun, T.: A new method for gray level picture threshold using the entropy of the histogram. Signal Process. 2(3), 223–237 (1980)

    Article  Google Scholar 

  37. Pun, T.: Entropic thresholding: a new approach. Comput. Graph. Image Process. 16, 210–239 (1981)

    Article  Google Scholar 

  38. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray level picture thresholding using the entropy of the histogram. Graph. Models Image Process. 29, 273–285 (1985)

    Google Scholar 

  39. Yen, J.C., Chang, F.J., Chang, S.: A new criterion for automatic multilevel thresholding. IEEE Trans. Image Process. IP–4, 370–378 (1995)

    Google Scholar 

  40. Sahoo, P., Wilkins, C., Yeager, J.: Threshold selection using Renyi’s entropy. Pattern Recognit. 30, 71–84 (1997)

    Article  MATH  Google Scholar 

  41. Li, C.H., Lee, C.K.: Minimum cross-entropy thresholding. Pattern Recognit. 26, 617–625 (1993)

    Google Scholar 

  42. Li, C.H., Tam, P.K.S.: An iterative algorithm for minimum cross-entropy thresholding. Pattern Recognit. Lett. 19, 771–776 (1998)

    Article  MATH  Google Scholar 

  43. Brink, D., Pendock, N.E.: Minimum cross entropy threshold selection. Pattern Recognit. 29, 179–188 (1996)

    Article  Google Scholar 

  44. Pal, N.R.: On minimum cross-entropy thresholding. Pattern Recognit. 29(4), 575–580 (1996)

    Article  Google Scholar 

  45. Shanbag, G.: Utilization of information measure as a means of image thresholding. Comput. Vis. Graph. Image Process. 56, 414–419 (1994)

    Article  Google Scholar 

  46. Cheng, H.D., Chen, Y.H., Sun, Y.: A novel fuzzy entropy approach to image enhancement and thresholding. Signal Process. 75, 277–301 (1999)

    Article  MATH  Google Scholar 

  47. Johannsen, G., Bille, J.: A threshold selection method using information measures. In: ICPR’82: Proceedings of 6th International Conference Pattern Recognition, pp. 140–143 (1982)

  48. Pal, S.K., King, R.A., Hashim, A.A.: Automatic gray level thresholding through index of fuzziness and entropy. Pattern Recognit. Lett. 1, 141–146 (1980)

    Article  Google Scholar 

  49. Li, Zuoyong, Liu, Chuancai, Liu, Guanghai, Cheng, Yong, Yang, Xibei, Zhao, Cairong: A novel statistical image thresholding method. Int. J. Electron. Commun. 64, 1137–1147 (2010)

    Article  Google Scholar 

  50. Tizhoosh, H.R.: Image thresholding using type II fuzzy sets. Pattern Recognit. 38, 2363–2372 (2005)

    Article  MATH  Google Scholar 

  51. Snidaro, L., Foresti, G.L.: Real-time thresholding with Euler numbers. Pattern Recognit. Lett. 24, 1533–1544 (2003)

    Article  MATH  Google Scholar 

  52. Papadopoulos, G.Th., Saathoff, C., Escalante, H.J., Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: A comparative study of object-level spatial context techniques for semantic image analysis. Comput. Vis. Image Underst. 115(9), 1288–1307 (2011)

    Google Scholar 

Download references

Acknowledgments

This paper was drawn out of the final report regarding a research project on “Foot Scanner Instrument Development” funded by Technical and Engineering Department, Garmsar Branch, Islamic Azad University, Garmsar, Iran, accomplished in the early 2012.

Author information

Authors and Affiliations

  1. Electrical Engineering Faculty, Islamic Azad University, Garmsar Branch, Garmsar, Iran

    Mehdi Siahi, Javad Razjouyan, Omid Khayat, Ali Alizadeh Mansouri & Zohre Azimi

  2. Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Omid Khayat

  3. Department of computer engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Ali Alizadeh Mansouri

  4. Neuromascular Research Laboratory, Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Zohre Azimi

Authors
  1. Mehdi Siahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javad Razjouyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omid Khayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Alizadeh Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zohre Azimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Siahi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Siahi, M., Razjouyan, J., Khayat, O. et al. A multi-class bi-level thresholding method for accurate anthropometric measurements of scanned plantar images. SIViP 9, 295–304 (2015). https://doi.org/10.1007/s11760-013-0440-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0440-7

Keywords

Search

Navigation