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Muthu Rama Krishnan M.,Indian Institute of Technology Kharagpur | Chakraborty C.,Indian Institute of Technology Kharagpur | Paul R.R.,Guru Nanak Institute of Dental Science and Research | Ray A.K.,Indian Institute of Technology Kharagpur
Expert Systems with Applications | Year: 2012

This work presents a quantitative microscopic approach for discriminating oral submucous fibrosis (OSF) from normal oral mucosa (NOM) in respect to morphological and textural properties of the basal cell nuclei. Practically, basal cells constitute the proliferative compartment (called basal layer) of the epithelium. In the context of histopathological evaluation, the morphometry and texture of basal nuclei are assumed to vary during malignant transformation according to onco-pathologists. In order to automate the pathological understanding, the basal layer is initially extracted from histopathological images of NOM (n = 341) and OSF (n = 429) samples using fuzzy divergence, morphological operations and parabola fitting followed by median filter-based noise reduction. Next, the nuclei are segmented from the layer using color deconvolution, marker-controlled watershed transform and gradient vector flow (GVF) active contour method. Eighteen morphological, 4 gray-level co-occurrence matrix (GLCM) based texture features and 1 intensity feature are quantized from five types of basal nuclei characteristics. Afterwards, unsupervised feature selection method is used to evaluate significant features and hence 18 are obtained as most discriminative out of 23. Finally, supervised and unsupervised classifiers are trained and tested with 18 features for the classification between normal and OSF samples. Experimental results are obtained and compared. It is observed that linear kernel based support vector machine (SVM) leads to 99.66% accuracy in comparison with Bayesian classifier (96.56%) and Gaussian mixture model (90.37%). © 2011 Elsevier Ltd. All rights reserved. Source


Muthu Rama Krishnan M.,Indian Institute of Technology Kharagpur | Choudhary A.,Indian Institute of Technology Kharagpur | Chakraborty C.,Indian Institute of Technology Kharagpur | Ray A.K.,Indian Institute of Technology Kharagpur | Paul R.R.,Guru Nanak Institute of Dental Science and Research
Micron | Year: 2011

The objective of this paper is to provide a texture based segmentation algorithm for better delineation of the epithelial layer from histological images in discriminating normal and oral sub-mucous fibrosis (OSF). As per literature and oral clinicians, it is established that the OSF initially originates and propagates in the epithelial layer. So, more accurate segmentation of this layer is extremely important for a clinician to make a diagnostic decision. In doing this, Gabor based texture gradient is computed in gray scale images, followed by preprocessing of the microscopic images of oral histological sections. On the other hand, the color gradients of these images are obtained in the transformed Lab color space. Finally, the watershed segmentation is extended to segment the layer based on the combination of texture and color gradients. The segmented images are compared with the ground truth images provided by the oral experts. The segmentation results depict the superiority of the texture based segmentation in comparison to the Otsu's based segmentation in terms of misclassification error. Results are shown and discussed. © 2011 Elsevier Ltd. Source


Muthu Rama Krishnan M.,Indian Institute of Technology Kharagpur | Shah P.,General Electric | Choudhary A.,Indian Institute of Technology Kharagpur | Chakraborty C.,Indian Institute of Technology Kharagpur | And 2 more authors.
Tissue and Cell | Year: 2011

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. © 2011 Elsevier Ltd. Source


Chaudhuri S.R.,CSIR - Central Electrochemical Research Institute | Mukherjee S.,CSIR - Central Electrochemical Research Institute | Paul R.R.,Guru Nanak Institute of Dental Science and Research | Haldar A.,National Medical College and Hospital | Chaudhuri K.,CSIR - Central Electrochemical Research Institute
Gene | Year: 2013

Chewing betel quid may release chemical carcinogens including xenobiotics resulting in oral malignancy cases preceded by potential malignant lesions and conditions - Oral Submucous Fibrosis (OSF) being one of them. The cytochrome P4501A1 (CYP1A1) enzyme is central to the metabolic activation of these xenobiotics, whereas CYP2E1 metabolizes the nitrosamines and tannins. The present study investigated the association of polymorphisms at CYP1A1m1 (T3801C), m2 (A2455G), and CYP2E1 PstI site (nucleotide 21259) with the risk of OSF. The study was conducted on 75 OSF patients and 150 controls from an eastern Indian population. The above polymorphisms were analyzed by PCR-RFLP method. Analyses of data show that polymorphisms in CYP1A1m2 [OR = 8.25 (4.31-15.80)]; CYP1A1m1 [OR = 2.88 (1.57-5.24)] and CYP2E1 PstI site [OR = 3.16 (1.10-9.04)] revealed significant association with OSF. Our results suggest that polymorphism in CYP1A1 and CYP2E1 may confer an increased risk for Oral Submucous Fibrosis. © 2012 Elsevier B.V. Source


Krishnan M.M.R.,Indian Institute of Technology Kharagpur | Chakraborty C.,Indian Institute of Technology Kharagpur | Paul R.R.,Guru Nanak Institute of Dental Science and Research | Ray A.K.,Indian Institute of Technology Kharagpur
Journal of Medical Imaging and Health Informatics | Year: 2011

This work presents a quantitative microscopic approach for discriminating inflammatory and fibroblast cells of oral submucous fibrosis (OSF) from normal oral mucosa (NOM) in respect to shape features of the sub-epithelial connective tissue (SECT) cells. However, malignancy develops only in the epithelium; significant pathological changes are evident in the SECT concurrently. The changes in SECT cell population will spell the intricate biological behaviour pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the present work characterizes the SECT cells (inflammatory and fibroblast) using their shape parameters. In this study segmentation and classification of sub-epithelial connective tissue (SECT) cells except endothelial cells in oral mucosa of normal and OSF conditions has been reported. Segmentation has been carried out by colour deconvolution and subsequently the cell population has been classified using Support Vector Machine (SVM) based classifier. Moreover, the shape features used in this study are statistically significant using Mann Whitney U test, which enhance the statistical learning potential and classification accuracy of the classifier. Automated classification of SECT cells characterizes this precancerous condition very precisely in a quantitative manner and unveils the opportunity to understand OSF related changes in cell population having definite geometric properties. The paper presents an automated classification method for understanding the deviation of SECT cell population from normal to precancerous stages. The SVM classifier is trained and tested with 15 features for the classification between normal and OSF samples. Experimental results are obtained and compared. It is observed that linear kernel based SVM identifies the unknown SECT cells with an average accuracy of 96.55% for normal, 94.65% for OSFWD and 92.33% for OSFD groups. This quantitative characterization of SECT cell population will be of immense help for oral onco-pathologists, researchers and clinicians to assess the biological behaviour of OSF, specially relating to their premalignant and malignant potentiality. Copyright © 2011 American Scientific Publishers. Source

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