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Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

Published online by Cambridge University Press:  17 September 2019

Debaleena Nawn ,
Saunak Chatterjee ,
Anji Anura ,
Swarnendu Bag ,
Debjani Chakraborty ,
Mousumi Pal ,
Ranjan Rashmi Paul  and
Jyotirmoy Chatterjee
Debaleena Nawn*
Affiliation:
Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Saunak Chatterjee
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Anji Anura
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Swarnendu Bag
Affiliation:
Tata Medical Center, Kolkata 700160, West Bengal, India
Debjani Chakraborty
Affiliation:
Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Mousumi Pal
Affiliation:
Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
Ranjan Rashmi Paul
Affiliation:
Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
Jyotirmoy Chatterjee
Affiliation:
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
*
*Author for correspondence: Debaleena Nawn, E-mail: [email protected]
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Abstract

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

Keywords

atomic force microscopyclassification accuracymicro-nano-textural attributesoral sub-mucous fibrosisscanning electron microscopy
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 5 , October 2019 , pp. 1224 - 1233
Copyright
Copyright © Microscopy Society of America 2019 

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