iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S10916-010-9550-8
Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images | Journal of Medical Systems Skip to main content

Advertisement

Springer Nature Link
Log in

Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images

  • ORIGINAL PAPER
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback–Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier’s performance from 80.69% to 90.75%. Results are here studied and discussed.

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
$34.99 /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 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

Similar content being viewed by others

References

  1. Jadhav, A. S. Banerjee, S., Dutta, P. K., Paul, R. R., Pal, M., Banerjee, P., Chaudhuri, K., and Chatterjee, J., Quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique, 19th IEEE Int. Symposium on Computer-Based Medical Systems, 231–236, 2006.

  2. Mukherjee, A., Paul, R. R., Chaudhuri, K., Chatterjee, J., Pal, M., and Banerjee, M., Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncol. 42:914–928, 2006.

    Article  Google Scholar 

  3. Tilakaratne, W., Klinikowski, M., Saku, T., Peters, T., and Warnakulasuriya, S., Oral submucous fibrosis: review on aetiology and pathogenesis. Oral Oncol. 42:561–568, 1997.

    Article  Google Scholar 

  4. Paul, R. R., Mukherjee, A., Dutta, P. K., Banerjee, S., Pal, M., Chatterjee, J., Chaudhuri, K., and Mukkerjee, K., Pathological stage detection for oral precancerous condition using a novel wavelet-neural network-based technique. J. Clin. Pathol. 58:932–938, 2005.

    Article  Google Scholar 

  5. Van Wyk, C. W., Seedat, H. A., and Philips, V. M., Collagen in submucous fibrosis: an electron microscopic study. J. Oral Pathol. Med. 19:182–187, 1990.

    Article  Google Scholar 

  6. Kong, J., Sertel, O., Shimada, H., Boyer, K. L., Saltz, J. H., and Gurcan, M. N., Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Pattern Recognit. 42:1080–1092, 2009.

    Article  Google Scholar 

  7. Wei, D., Chan, H.-P., Helvie, M. A., Sahiner, B., Petrick, N., Adler, D. D., and Goodsitt, M. M., Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms. Proc. SPIE. 2434:606–611, 1995.

    Article  Google Scholar 

  8. Esgiar, A. N., Naguib, R. N. G., Sharif, B. S., Bennett, M. K., and Murray, A., Fractal analysis in the detection of colonic cancer images. IEEE Trans. Inf. Technol. Biomed. 6:54–58, 2002.

    Article  Google Scholar 

  9. Pitts, D. E., Prem kumar, S. B., Houston, A. G., Babaian, R. J., and Troncosa, P., Texture analysis of digitized prostate pathologic cross section. Proc. SPIE: Med. Imaging: Image Process. 1898:465–470, 1993.

    Google Scholar 

  10. Gonzalez, R. C., Woods, R. E., Digital image processing, 2nd Edition, Prentice Hall, 2002.

  11. Duda, R., Hart, P., Stork, D., Pattern classification, Wiley India, 2nd Edn. 2007.

  12. Mandelbrot, B.B., The fractal geometry of nature. New York: WH Freeman Ed. 1982.

  13. Biswas, M. K., Ghose, T., Guha, S., Biswas, P. K., Fractal dimension estimation for texture images: A parallel approach, Pattern Recognition Letters. 19;3–4; 309–313; 1998.

    Google Scholar 

  14. Lundahl, T., Ohley, W. J., Kay, S. M., and Siffert, R., Fractional Brownian motion: a maximum likelihood estimator and its application to image texture. IEEE Trans. Med. Imaging 5:152–161, 1986.

    Article  Google Scholar 

  15. Hurst, H. E., Black, R.,P., Simaika, Y. M., Long-term storage, an experimental study, constable, London. 1965.

  16. Chen, E. L., Chung, P. C., Chen, C. L., Tsai, H. M., and Chang, C. I., An automatic diagnostic system for CT liver image classification. IEEE Trans. Biomed. Eng. 45(6):783–794, 1998.

    Article  Google Scholar 

  17. Huang, C. L., Liao, H. C., and Chen, M. C., Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst. Appl. 34:578–587, 2008.

    Article  Google Scholar 

  18. Bressan, M., and Vitria, J., On the selection and classification of independent features. IEEE Trans. Pattern Anal. Mach. Intell. 25:1312–1317, 2003.

    Article  Google Scholar 

  19. http://www.cs.cmu.edu/∼schneide/tut5/node42.html last accessed December 2009.

  20. Polat, K., and Güneş, S., Breast cancer diagnosis using least square support vector machine. Digit. Signal Process. 17(4):694–701, 2007.

    Article  Google Scholar 

  21. Kendall, M. G., and Stuart, A., The advanced theory of statistics, 1. Griffin, London, 1969.

    Google Scholar 

  22. Jarque, C. M., and Bera, A. K., A test for normality of observations and regression residuals. Int. Stat. Rev. 55(2):163–172, 1987.

    Article  MathSciNet  MATH  Google Scholar 

  23. Gun, A. M., Gupta, M. K., Dasgupta, B., An outline of statistical theory (Vol I& II), world press private Ltd, 4th edition, 2005.

  24. Gun, A. M., Gupta, M. K., Dasgupta, B., Fundamentals of statistics (Vol I& II), world press private Ltd, 4th edition, 2008.

  25. Fletcher, S. J., and Zupanski, M., A hybrid multivariate normal and lognormal distribution for data assimilation. Atmos. Sci. Lett. 7:43–46, 2006.

    Article  Google Scholar 

  26. Christopher, J. C., and Burges, A., A tutorial on support vector machines for patternrecognition. Data Min. Knowl. Discov. 2:121–167, 1998.

    Article  Google Scholar 

  27. Vapnik, V., Statistical learning theory, 2nd edition. Wiley, New York, 1998.

    MATH  Google Scholar 

  28. Huang, C. L., Liao, H. C., and Chen, M. C., Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst. Appl. 34:578–587, 2008.

    Article  Google Scholar 

  29. El-Naqa, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., and Nishikawa, M. R., A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imag. 21:1552–1563, 2002.

    Article  Google Scholar 

  30. Gunn Steve, R., Support vector machines for classification and regression, Technical report. 1–66, 1998.

  31. Hilbert, C., Methods of mathematical physics. Wiley Interscience, New York, 1953.

    Google Scholar 

Download references

Acknowledgement

The authors would like to thank Prof. R. R. Paul and Dr. M. Pal, GNDSIR, Kolkata, India, and Dr. J. Chatterjee, SMST, IIT Kharagpur for their clinical support and valuable advices.

Author information

Author notes

  1. Pratik Shah

    Present address: JFWTC, GE, Research, Bangalore, India

Authors and Affiliations

  1. School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India

    M. Muthu Rama Krishnan, Pratik Shah & Chandan Chakraborty

  2. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India

    Ajoy K. Ray

Authors
  1. M. Muthu Rama Krishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pratik Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandan Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ajoy K. Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandan Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Muthu Rama Krishnan, M., Shah, P., Chakraborty, C. et al. Statistical Analysis of Textural Features for Improved Classification of Oral Histopathological Images. J Med Syst 36, 865–881 (2012). https://doi.org/10.1007/s10916-010-9550-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9550-8

Keywords

Search

Navigation