CFP last date
20 May 2024
Reseach Article

Textural Analysis of Spinous Layer for Grading Oral Submucous Fibrosis

by Rusha Patra, Chandan Chakraborty, Jyotirmoy Chatterjee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 22
Year of Publication: 2012
Authors: Rusha Patra, Chandan Chakraborty, Jyotirmoy Chatterjee
10.5120/7513-0563

Rusha Patra, Chandan Chakraborty, Jyotirmoy Chatterjee . Textural Analysis of Spinous Layer for Grading Oral Submucous Fibrosis. International Journal of Computer Applications. 48, 22 ( June 2012), 33-37. DOI=10.5120/7513-0563

@article{ 10.5120/7513-0563,
author = { Rusha Patra, Chandan Chakraborty, Jyotirmoy Chatterjee },
title = { Textural Analysis of Spinous Layer for Grading Oral Submucous Fibrosis },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 22 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number22/7513-0563/ },
doi = { 10.5120/7513-0563 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:46.482348+05:30
%A Rusha Patra
%A Chandan Chakraborty
%A Jyotirmoy Chatterjee
%T Textural Analysis of Spinous Layer for Grading Oral Submucous Fibrosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 22
%P 33-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spinous layer being a sub compartment of surface epithelium of oral mucosa, plays also major role in investigating oral submucous fibrosis (OSF) at its early stage in addition to basal layer. This paper aims to provide a technique that can be used to assist the oral pathologists in grading OSF based on textural information of the spinous layer. The proposed scheme intends to evaluate the textural changes from normal to various grades of OSF. In practice, it comprises the following modules – (a) surface epithelium segmentation, (b) selection of windows on the spinous layer in reference to the basal layer, (c) textural feature extraction and analysis and finally (d) grading. Here the epithelium is segmented using anisotropic diffusion and Otsu's thresholding. Wavelet based multi-resolution technique is applied to extract 12 textural features from spinous layer. From the statistical analysis, it is observed that 6 features are significant in discriminating normal, OSF with and without dysplasia. Finally, support vector machine (SVM) and Bayesian classifiers are trained with 46 normal, 24 OSF without dysplasia and 20 OSF with dysplasia samples for OSF grading. The result shows that the classification accuracies for both the classifiers (Bayesian = 93. 3%, SVM = 96. 6%) are comparable, there by emphasizing the significance of texture in oral cancer diagnostics.

References
  1. Jadhav, A. S. , Banerjee, S. , Dutta, P. K. , Paul, R. R. , and Pal, M. 2006. Quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. 19th IEEE Int. Symposium on Computer-Based Medical Systems, 231-236.
  2. Mukherjee, A. , Paul, R. R. , Chaudhri, K. , Chatterjee, J. , Pal, M. , and Banerjee, P. 2006. Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncology, 42, 914-928.
  3. Nanci, A. 2008. Oral Mucosa, Ten Cate's Oral Histology. Indian: Mosby- Elsevier.
  4. Pindborg J. J. , and Sirsat, S. M. 1966. Oral submucous fibrosis, Oral Surg. Oral Med. Oral Pathol. 22, pp. 764–779
  5. Cawson, R. A. , and Odell, E. 2002. Cawson's Essentials of Oral Pathology and Oral Medicine. Churchill Livingstone.
  6. Landini, G. , and Rippin, J. W. 1996. How important is tumour shape? Quantification of the epithelial-connective tissue interface in oral lesions using local connected fractal dimension analysis. The Journal of Pathology, 210-217.
  7. Landini, G. , and Othman, I. E. 2004. Architectural analysis of oral cancer, dysplastic and normal epitheliam. Cytometry, 45-55.
  8. Abu-Eid, R. , and Landini, G. 2006. Oral Epithelial Dysplasia: Can quantifiable morphological features help in the grading dilemma? First Image User and Developer Conference. Luxembourg.
  9. Kayser, K. , Sandau, K. , Bohm, G. , Kunze, K. , and Paul J, J. 1991. Analysis of soft tissue tumors by an attributed minimum spanning tree. Analytical & Quantitative Cytology & Histology, 329-334.
  10. Marcelpoil, R. , Davoine, F. , and Robert-Nicaud, M. 1994. Cellular sociology: parametrization of spatial relationships based on Voronoi diagram and Ulam trees. Fractals in biology and medicine, 201-209.
  11. Paul, R. R., Mukherjee, A., Dutta, P. K., Banerjee, S., Pal, M., Chatterjee, J., Mukhopadhyay, K, and Chaudhuri, K.2005. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition. Journal of Clinical Pathology, 58, 932-938.
  12. Mukherjee, A. , Paul, R. R. , Chaudhri, K. , Chatterjee, J. , Pal, M. , and Banerjee, P. 2006. Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncology, 42, 914 – 928.
  13. M. M. R. K. , Pal, M. , Bomminayuni, S. K. , Chakraborty, C. , Paul, R. R. , Chatterjee, J. , Ray, A. K. 2009. Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis—An SVM based approach, Computers in Biology and Medicine, 39(12), 1096-1104.
  14. Muthu Rama Krishnan, M. , Shah, P. , Pal, M. , Chakraborty, C. , Paul, R. R. , Chatterjee, J. , Ray, A. K. 2010. Structural markers for normal oral mucosa and oral sub-mucous fibrosis. Micron 41 (4), 312–320.
  15. Perona, P. , and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12 (7).
  16. Montagna W, Parakkal PF. 1974. The Structure and Function of Skin, 3rd edn. New York: Academic Press.
  17. Mallat, S. 1989. A theory for multiresolution signal decomposition: the wavelet representation, IEEE transaction on Pattern Analysis and machine intelligence. 11(7), pp 674-693.
  18. Gun, A. M. , Gupta, M. K. , Dasgupta, B. 2008. Fundamentals of Statistics. Vol. 2. World Press.
  19. Jarque, C. M. , Bera, A. K. 1987. A test for normality of observations and regression residuals. International Statistical Review, 55(2), 163-172.
  20. Duda, R. , Hart, P. , & Stork, D. 2007. Pattern classification. Wiley India.
  21. Vapnik, V. 1998. Statistical learning theory. New york: Wiley.
  22. Gunn, S. R. 1998. Support Vector Machines for Classification and Regression.
Index Terms

Computer Science
Information Sciences

Keywords

Oral Submucous Fibrosis (osf) Spinous Layer Anisotropic Diffusion Wavelet Statistical Test Bayesian Classifier Support Vector Machine.