摘要
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.
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.
