NS  Vol.3 No.11 , November 2011
Gamma-ray compton spectrum analysis to enhance medical imaging using wavelet transformation
Abstract: Cs-137 radioactive source with 661.7 keV gamma- ray energy and Am-241 with 59.5 keV gamma-ray energy were used to study the body structure of materials by examining transmitted gamma-ray spectrum using a scintillation detector, NaI(Tl). Due to specific characteristic properties of the medium, the passing Compton broad scattering spectrum contains valuable information. It is possible to mark and to specify the Compton spectrum caused by atomic specifications of Al, Cu, bone, muscle, and lipid as interactive materials. Wavelet transforms and other multi-scale analysis functions have been used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years. Comparing the transmitted spectra through muscle, bone and a tumor-like (fat) and analyzing each spectrum by wavelet analysis, the differences of the medium were shown. This study is devoted to use of wavelet transform for feature extraction associated with gamma spectrum, which corresponds to image pixel, and their classification in comparison with the Haar and Rbio3.1 transforms.
Cite this paper: Ebrahimi, S. and Pazirandeh, A. (2011) Gamma-ray compton spectrum analysis to enhance medical imaging using wavelet transformation. Natural Science, 3, 963-970. doi: 10.4236/ns.2011.311123.

[1]   Xianga, J.W., Chenb, X.F., Moa, Q.Y. and Heb, Z.J. (2007) Identification of crack in a rotor system based on wavelet finite element method. The State Key Laboratory for Manufacturing Systems Engineering, Xi’an.

[2]   Chang, C.-C. and Chen, L.-W. (2005) Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach. Mechanical Systems and Signal Processing, 19, 139-155. doi:10.1016/j.ymssp.2003.11.001

[3]   Yeh, P.-L. and Liu, P.-L. (2008) Application of the wavelet transform and the enhanced Fourier spectrum in the impact echo test. NDT&E International, 41, 382-394. doi:10.1016/j.ndteint.2008.01.002

[4]   Krug, R., Carballido-Gamio, J., Burgardi, A.J., Haase, S., Sedat, J.W., Moss, W.C. and Majumdar, S. (2007) Wave- let-based characterization of verbecular bone structure from magnetic resonance images at 3 T compared with micro-computed tomography measurements, Magnetic Resonance Imaging, 25, 392-398. doi:10.1016/j.mri.2006.09.020

[5]   Sullivan, C.J., Garner, S.E., Blagoev, K.B. and Weiss, D.L. (2007) Generation of customized wavelets for the analysis of γ-ray spectra. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 579, 275-278. doi:10.1016/j.nima.2007.04.055

[6]   Hariharan, G. and Kannan, K. (2009) Haar wavelet method for solving Cahn-Allen Equation. Applied Mathematical Sciences, 3, 2523-2533.

[7]   Lee, Y.-S. (2009) Principles of terahertz science and technology. Springer, Berlin, 1, 261-262.

[8]   Nandhakumar, S., Selladurai, V., Muthukumaran, V. Sekar, S. (2010) Haar wavelet approach to time varying model robot arm control problem. International Journal of Academic Research, 2.