JBiSE  Vol.5 No.12 A , December 2012
Raman spectroscopy for human cancer tissue diagnosis: A pattern recognition approach
ABSTRACT
In this work, optical scattering using Raman spectroscopy has been analyzed for various cancer tissues. The Raman shifts obtained at the Indiana University Bloomington (IUB) and Indiana University-Purdue University Indianapolis (IUPUI) laboratories have been processed for diagnosing various types of cancer tissues. The objective of this research is to distinguish between cancerous and non-cancerous tissues. Small size tissue samples have been processed, seeking the minimum size tissue that can be diagnosed via Raman spectroscopy. The tests have been conducted on nearly 20 human tissues. A Matlab program has been written following Parzen-Window classifier to recognize the Raman shift pattern for various types of cancer tissues, including breast cancer, kidney, and Gyn-Uterus. A software visual model has been used for data processing. Unique signals for breast and kidney tumors have been obtained. The approach followed in this paper shows promise for early cancer detection in humans.

Cite this paper
Rizkalla, M. , Ghane, P. , Agarwal, M. , Shrestha, S. and Varahramyan, K. (2012) Raman spectroscopy for human cancer tissue diagnosis: A pattern recognition approach. Journal of Biomedical Science and Engineering, 5, 892-900. doi: 10.4236/jbise.2012.512A113.
References
[1]   Haka, A.S., Shafer-Peltier, K.E., Fitzmaurice, M., Crowe, J., Dasari, R.R. and Feld, M.S. (2002) Detecting breast cancer using Raman spectroscopy. Proceedings of Bio- medical Topical Meeting, Miami, 7 April 2002.

[2]   Upile, T., et al. (2009) “Head & neck optical diagnosis: Vision of the future. Head and Neck Oncology, 1, 1-9.

[3]   Swain R.J. and Steven, M.M. (2007) Raman micro spec- troscopy for non-invasive biochemical analysis of single cells. Biochemical Society Transactions, 35, 544-549. doi:10.1042/BST0350544

[4]   Haishan, Z., et al. (2009) Raman Spectroscopy for in-vivo toissue analysis and diagnosis at the macro-and micro le- vels. Proceedings of Communications and Photonic Con- ference and Exhibition (ACP), Shanghai, 2-6 November 2009.

[5]   Fenn, M.B., et al. (2011) Raman spectroscopy for clini- cal oncology. Advances in Optical Technologies, 1-20. doi:10.1155/2011/213783

[6]   Stone, N., Kerssens, M., Lloyd, G.R., Faulds, K., Graham, D. and Matousek, P. (2011) Surface enhanced spatially offset raman spectroscopic (sesors) imaging: The next di- mension. Chemical Science, 2, 776-780. doi:10.1039/c0sc00570c

[7]   (2012) Breast cancer. American Society of Clinical Oncology. http://www.cancer.net/patient/Cancer+Types/Breast+Cancer

[8]   (2012) Raman spectroscopy general overview. Omega Optical. http://www.omegafilters.com/Capabilities/Applications/Raman_Spectroscopy/Raman_General

[9]   Wolf, J.G. (2006) Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search. Decision Support Sy- stem, 42, 608-625. doi:10.1016/j.dss.2005.02.005

[10]   Begum, S.A. and Devi, O.M. (2011) Fuzzy algorithms for pattern recognition in medical diagnosis. Physics Sci- ences and Technologies, 7, 1-12. doi:10.1111/1467-9876.00075

[11]   Purushotham T. and Rao, R. (2010) Pattern recognition diagnostics for emphysema using phase only correlation technique. Proceedings of 42nd South Eastern Sympos- ium on System Theory, Tyler, 7-9 March 2010.

[12]   Zhang, Q.P., Liang, M. and Sun, W.C. (2004) Medical diagnostic image fusion based on feature mapping wave- let neural networks. Proceedings of the 3rd International Conference and Graphics, Shanghai, 18-20 December 2004.

[13]   Wright, D., Stander, J. and Nicolaides, K. (1997) Non- parametric density estimation and discrimination from images of shapes. Journal of the Royal Statistical Society, Series C: Applied Statistics, 46, 365-380.

[14]   Frankt, C.J. and McCreey, R.I. (1995) Raman spectro- scopy of normal and diseased human breast tissues. Analytical Chemistry, 67, 777-783. doi:10.1021/ac00101a001

 
 
Top