Wavelet based texture classification of oral histopathological sections

In modern medical science, quantitative microscopy has been attracted the researchers for better understanding, visualization and evaluation of the biological samples, especially in the context of oral histopathological evaluation. The conventional light microscopy is, of course, a routine diagnostic method till date where lot of subjectivity is involved in terms of stain intensity, interand intra-observer variations, which lead to higher misclassification error. In practice, the oral onco-pathologists usually visualize the intensity and morphological changes in the tissue and cellular level in order to diagnose oral pre-cancer and cancer. Among various precancerous conditions, oral sub-mucous fibrosis has high degree of malignant potentiality and hence it should be screened at its early stage. In addition to morphology and intensity of tissue structure, textural information play significant role over epithelial regions of normal and abnormal tissue samples; but visualization of its variation through human eyes are more cumbersome task.