Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach

Computers in Biology and Medicine

Volumn 39, Issue 12, 2009, Pages 1096-1104

  Muthu Rama Krishnan, M ^a   Pal, Mousumi ^b   Bomminayuni, Suneel K ^a   Chakraborty, Chandan ^a   Paul, Ranjan Rashmi ^b   Chatterjee, Jyotirmoy ^a   Ray, Ajoy K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b Dr R Ahmed Dental College and Hospital   (India)

Author keywords

Multi level thresholding; Oral sub mucous fibrosis (OSF); Sub epithelial connective tissue (SECT); Support vector machine (SVM)

Indexed keywords

AUTOMATED CLASSIFICATION; CELL POPULATIONS; CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSTICS; CONNECTIVE TISSUES; GEOMETRIC FEATURE; GEOMETRIC PROPERTIES; IN-CELL; MULTILEVEL THRESHOLDING; ORAL MUCOSA; QUANTITATIVE EVALUATION; STATISTICAL LEARNING; STRUCTURAL PROFILES;

AUTOMATION; CELL CULTURE; CELL PROLIFERATION; CLASSIFIERS; COLLAGEN; IMAGE RETRIEVAL; MUSCULOSKELETAL SYSTEM; SUPPORT VECTOR MACHINES;

ENDOTHELIAL CELLS;

ANIMAL CELL; ANIMAL TISSUE; ARTICLE; CELL POPULATION; CONNECTIVE TISSUE CELL; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DISEASE CLASSIFICATION; HISTOPATHOLOGY; HUMAN; LABORATORY AUTOMATION; MOUTH DISEASE; NONHUMAN; PRECANCER; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONNECTIVE TISSUE; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSTIC ERRORS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MOUTH MUCOSA; MOUTH NEOPLASMS; ORAL SUBMUCOUS FIBROSIS; PRECANCEROUS CONDITIONS;

EID: 71849093026     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2009.09.004     Document Type: Article

References (31)

1. A.S. Jadhav, S. Banerjee, P.K. Dutta, R.R. Paul, M. Pal, P. Banerjee, et al., Quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique, in: 19th IEEE International Symposium on Computer-Based Medical Systems, vol. 25, 2006, pp. 231-236.
2. Mukherjee A., Paul R.R., Chaudhuri K., Chatterjee J., Pal M., and Banerjee P. Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncol 42 (2006) 914-928
3. Tilakaratne W., Klinikowski M., Saku T., Peters T., and Warnakulasuriya S. Oral submucous fibrosis: review on aetiology and pathogenesis. Oral Oncol 42 (1997) 561-568
4. 〈http://www.maxillofacialcenter.com/BondBook/softtissue/submucousfi brosis.html〉.
5. Seedat H.A., and van Wyk C.W. Betel-nut chewing and submucous fibrosis in Durban. S. Afr. Med. J. 7 (1988) 568-571
6. Canniff J.P., and Harvey W. The aetiology of oral submucous fibrosis: the stimulation of collagen synthesis by extracts of areca nut. Int. J. Oral Surg. 10 1 (1981) 163-167
7. Aziz S.R. Oral submucous fibrosis: an unusual disease. J. N. J. Dent. Assoc. 68 (1997) 17-19
8. Canniff J.P., Harvey W., and Harris M. Oral submucous fibrosis: its pathogenesis and management. Br. Dent. J. 160 (1986) 429-434
9. Maher R., Lee A.J., Warnakulasuriya K.A., et al. Role of areca nut in the causation of oral submucous fibrosis: a case-control study in Pakistan. J. Oral Pathol. Med. 23 (1994) 65-69
10. Zain R.B., Ikeda N., Razak I.A., et al. A national epidemiological survey of oral mucosal lesions in Malaysia,. Community Dent. Oral Epidemiol. 25 (1997) 377-383
11. Paul R.R., Chatterjee J., Das A.K., Cervera M.L., Guardia M., and Chaudhuri K. Altered elemental profile as indicator of homeostatic imbalance in pathogenesis of oral submucous fibrosis. Biol. Trace Elem. Res. 87 (2002) 45-56
12. Paul R.R., Chatterjee J., Das A.K., Dutta S.K., and Roy D. Zinc and iron as bioindicators of precancerous nature of oral submucous fibrosis. Biol. Trace Elem. Res. 54 (1996) 213-230
13. Paul R.R., Mukherjee A., Dutta P.K., Banerjee S., Pal M., Chatterjee J., et al. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition. J. Clin. Pathol. 58 (2005) 932-938
14. Pindborg J.J., and Sirsat S.M. Oral submucous fibrosis. Oral Surg. Oral Med. Oral Pathol. 22 (1966) 764-779
15. Grizzi C.-I. The complexity of anatomical systems. Theor. Biol. Med. Modeling 26 (2005) 1-9
16. Schock F., and Perrimon N. Molecular mechanisms of epithelial morphogenesis. Annu. Rev. Cell Dev. Biol. 18 (2002) 463-493
17. Goldmann W.H. Mechanical aspects of cell shape regulation and signaling. Cell. Biol. Int. 26 (2002) 313-317
18. Antonio N. Oral Mucosa Ten Cate's Oral Histology (2008), Mosby-Elsevier, Indian Edn. pp. 319-356
19. Ankle Madhuri R., Kale Alka D., and Nayak R. Mast cells are increased in leukoplakia, oral submucous fibrosis, oral lichen planus and oral squamous cell carcinoma. J. Oral Maxillofacial Pathol. 11 (2007) 18-22
20. De Waal J., Olivier A., van Wyk C.W., and Maritz J.S. The fibroblast population in oral submucous fibrosis. J. Oral Pathol. Med. 26 (1997) 69-74
21. Vapnik V. Statistical learning theory. second ed. (1998), Wiley, New York
22. El-Naqa I., Yang Y., Wernick M.N., Galatsanos N.P., and Nishikawa R.M. A support vector machine approach for detection of micro calcifications. IEEE Trans. Med. Imaging 21 (2002) 1552-1563
23. Metehan M. Support vector approach for classification of cancerous prostrate regions. PWASET 7 (2005) 166-169
24. Liu Z., Zhang D., Yan J.-q., Li Q.-L., and Tang Q.-l. Classification of hyperspectral medical tongue images for tongue diagnosis. Computerized Med Imaging Graphics 31 (2007) 672-678
25. M.F. Akay, Support vector machines combined with feature selection for breast cancer diagnosis, Expert Systems with Applications 36 (2) (2009) 3240-3247.
26. Hung C.-L., Liao H.-C., and Chen M.-C. Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Systems with Applications 34 (2008) 578-587
27. Acharya T., and Ray A.K. Image Processing-Principles and Applications (2005), Wiley, New Jersey
28. Gonzalez R.C., and Woods R.E. Digital Image Processing. second ed. (2002), Prentice-Hall, New York
29. Chaudari D., and Samal A. A simple method for fitting of bounding rectangle to closed regions. Pattern Recognition 40 (2007) 1981-1989
30. Andrew R., and Webb T. Statistical Pattern Recognition (2003), Wiley, New York
31. Kumar S. Neural Networks: A Classroom Approach. second ed. (2003), Tata McGraw-Hill