Statistical analysis of textural features for improved classification of oral histopathological images

Journal of Medical Systems

Volumn 36, Issue 2, 2012, Pages 865-881

  Krishnan, M Muthu Rama ^a   Shah, Pratik ^a   Chakraborty, Chandan ^a   Ray, Ajoy K ^a,b  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b GE GLOBAL RESEARCH   (India)

Author keywords

Bayesian classifier; Collagen; Oral cancer; Statistical tests; Support vector machine; Texture

Indexed keywords

COLLAGEN FIBER; COLLAGEN;

CONTROLLED STUDY; DISEASE CLASSIFICATION; HISTOPATHOLOGY; HUMAN; HUMAN CELL; HUMAN TISSUE; IMAGE ANALYSIS; LEUKOPLAKIA; MOUTH DISEASE; REVIEW; STATISTICAL ANALYSIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLASSIFICATION; CONNECTIVE TISSUE; EARLY DIAGNOSIS; IMAGE PROCESSING; METHODOLOGY; MOUTH MUCOSA; MOUTH TUMOR; NORMAL DISTRIBUTION; PATHOLOGY; PRECANCER; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

BAYES THEOREM; COLLAGEN; CONNECTIVE TISSUE; EARLY DETECTION OF CANCER; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MOUTH MUCOSA; MOUTH NEOPLASMS; NEURAL NETWORKS (COMPUTER); NORMAL DISTRIBUTION; ORAL SUBMUCOUS FIBROSIS; PRECANCEROUS CONDITIONS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 84863197629     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9550-8     Document Type: Review

References (31)

1. Jadhav,A. S. Banerjee, S.,Dutta, P.K., Paul, R. R., Pal,M., Banerjee, P., Chaudhuri, K., and Chatterjee, J., 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, 2006. (Pubitemid 44929993)
2. Mukherjee, A., Paul, R. R., Chaudhuri, K., Chatterjee, J., Pal, M., and Banerjee, M., Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition. Oral Oncol. 42:914-928, 2006. (Pubitemid 44462752)
3. Tilakaratne, W., Klinikowski, M., Saku, T., Peters, T., and Warnakulasuriya, S., Oral submucous fibrosis: review on aetiology and pathogenesis. Oral Oncol. 42:561-568, 1997.
4. Paul, R. R., Mukherjee, A., Dutta, P. K., Banerjee, S., Pal, M., Chatterjee, J., Chaudhuri, K., and Mukkerjee, K., Pathological stage detection for oral precancerous condition using a novel wavelet-neural network-based technique. J. Clin. Pathol. 58:932-938, 2005. (Pubitemid 41265277)
5. Van Wyk, C. W., Seedat, H. A., and Philips, V. M., Collagen in submucous fibrosis: an electron microscopic study. J. Oral Pathol. Med. 19:182-187, 1990. (Pubitemid 20228780)
6. Kong, J., Sertel, O., Shimada, H., Boyer, K. L., Saltz, J. H., and Gurcan, M. N., Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Pattern Recognit. 42:1080-1092, 2009.
7. Wei, D., Chan, H.-P., Helvie, M. A., Sahiner, B., Petrick, N., Adler, D. D., and Goodsitt, M. M., Multiresolution texture analysis for classification of mass and normal breast tissue on digital mammograms. Proc. SPIE. 2434:606-611, 1995.
8. Esgiar, A. N., Naguib, R. N. G., Sharif, B. S., Bennett, M. K., and Murray, A., Fractal analysis in the detection of colonic cancer images. IEEE Trans. Inf. Technol. Biomed. 6:54-58, 2002. (Pubitemid 34491595)
9. Pitts, D. E., Prem kumar, S. B., Houston, A. G., Babaian, R. J., and Troncosa, P., Texture analysis of digitized prostate pathologic cross section. Proc. SPIE: Med. Imaging: Image Process. 1898:465-470, 1993.
10. Gonzalez, R. C., Woods, R. E., Digital image processing, 2nd Edition, Prentice Hall, 2002.
11. Duda, R., Hart, P., Stork, D., Pattern classification, Wiley India, 2nd Edn. 2007.
12. Mandelbrot, B.B., The fractal geometry of nature. New York: WH Freeman Ed. 1982.
13. Biswas, M. K., Ghose, T., Guha, S., Biswas, P. K., Fractal dimension estimation for texture images: A parallel approach, Pattern Recognition Letters. 19;3-4; 309-313; 1998.
14. Lundahl, T., Ohley, W. J., Kay, S. M., and Siffert, R., Fractional Brownian motion: a maximum likelihood estimator and its application to image texture. IEEE Trans. Med. Imaging 5:152-161, 1986.
15. Hurst, H. E., Black, R.,P., Simaika, Y. M., Long-term storage, an experimental study, constable, London. 1965.
16. Chen, E. L., Chung, P. C., Chen, C. L., Tsai, H. M., and Chang, C. I., An automatic diagnostic system for CT liver image classification. IEEE Trans. Biomed. Eng. 45(6):783-794, 1998.
17. Huang, C. L., Liao, H. C., and Chen, M. C., Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst. Appl. 34:578-587, 2008.
18. Bressan, M., and Vitria, J., On the selection and classification of independent features. IEEE Trans. Pattern Anal. Mach. Intell. 25:1312-1317, 2003.
19. http://www.cs.cmu.edu/-schneide/tut5/node42.html last accessed December 2009.
20. Polat, K., and Güneş, S., Breast cancer diagnosis using least square support vector machine. Digit. Signal Process. 17(4):694-701, 2007. (Pubitemid 46819053)
21. Kendall, M. G., and Stuart, A., The advanced theory of statistics, 1. Griffin, London, 1969.
22. Jarque, C. M., and Bera, A. K., A test for normality of observations and regression residuals. Int. Stat. Rev. 55(2):163-172, 1987.
23. Gun, A. M., Gupta, M. K., Dasgupta, B., An outline of statistical theory (Vol I& II), world press private Ltd, 4th edition, 2005.
24. Gun, A. M., Gupta, M. K., Dasgupta, B., Fundamentals of statistics (Vol I& II), world press private Ltd, 4th edition, 2008.
25. Fletcher, S. J., and Zupanski, M., A hybrid multivariate normal and lognormal distribution for data assimilation. Atmos. Sci. Lett. 7:43-46, 2006. (Pubitemid 43905384)
26. Christopher, J. C., and Burges, A., A tutorial on support vector machines for patternrecognition. Data Min. Knowl. Discov. 2:121-167, 1998.
27. Vapnik, V., Statistical learning theory, 2nd edition. Wiley, New York, 1998.
28. Huang, C. L., Liao, H. C., and Chen, M. C., Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst. Appl. 34:578-587, 2008.
29. El-Naqa, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., and Nishikawa, M. R., A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imag. 21:1552-1563, 2002.
30. Gunn Steve, R., Support vector machines for classification and regression, Technical report. 1-66, 1998.
31. Hilbert, C., Methods of mathematical physics. Wiley Interscience, New York, 1953.