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Textural characterization of histopathological images for oral sub-mucous fibrosis detection
Authors:Krishnan M Muthu Rama  Shah Pratik  Choudhary Anirudh  Chakraborty Chandan  Paul Ranjan Rashmi  Ray Ajoy K
Institution:aSchool of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, 721302, India;bJFWTC, GE, Research, Bangalore, India;cDepartment of Electrical Engineering, IIT Kharagpur, India;dDepartment of Oral and Maxillofacial Pathology, Guru Nanak Institute of Dental Science and Research, Kolkata, India;eDepartment of Electronics and Electrical Communication Engineering, IIT Kharagpur, India
Abstract:In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly. In totality, 71 textural features are extracted from epithelial region of the tissue sections using various wavelet families, Gabor-wavelet, local binary pattern, fractal dimension and Brownian motion curve, followed by preprocessing and segmentation. Wavelet families contribute a common set of 9 features, out of which 8 are significant and other 61 out of 62 obtained from the rest of the extractors are also statistically significant (p < 0.05) in discriminating the three stages. Based on mean distance criteria, the best wavelet family (i.e., biorthogonal3.1 (bior3.1)) is selected for classifier design. support vector machine (SVM) is trained by 146 samples based on 69 textural features and its classification accuracy is computed for each of the combinations of wavelet family and rest of the extractors. Finally, it has been investigated that bior3.1 wavelet coefficients leads to higher accuracy (88.38%) in combination with LBP and Gabor wavelet features through three-fold cross validation. Results are shown and discussed in detail. It is shown that combining more than one texture measure instead of using just one might improve the overall accuracy.
Keywords:Abbreviations: OSF  oral submucous fibrosis  LBP  local binary pattern  SVM  support vector machine  OED  oral epithelial dysplasia  FD  fractal dimension  BMC  Brownian motion curve  DWT  discrete wavelet transform  OSFWD  OSF without dysplasia  OSFD  OSF with dysplasia  HE  haematoxylin and eosin  CAD  computer aided diagnostic  AFP  area under first peak  NP  total number of peaks  MDP  mean distance between peaks  ANOVA  analysis of variance
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