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

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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.

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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.

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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

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  1. M. Muthu Rama Krishnan
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  2. Pratik Shah
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Correspondence to Chandan Chakraborty.

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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

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

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