A Peak Detection in Noisy Spectrum Using Principal Component Analysis

A spectrum of a radio isotope (Rl) contains a single or multiple photo-peaks and radio-activities of all energy levels. These characteristics of each RI source are measured by radiation monitor (RM) systems. However, if the radiation count is extremely low and source to detector distance is too far, we cannot acquire good spectroscopic results for RI identification by RM devices while we still able to measure some counting statistics. Thus, precise peak detection in noisy spectrums is one of the most important tasks in the RM system. In this study, we developed an accurate peak detection method based on wavelet decomposition followed by principal component analysis. We used a discrete wavelet transform (DWT) for reduction of unnecessary high frequency noises in low counts spectrums. To reduce effect of a background radiation, we made a background template using a pre-measured background spectrum and calculated square errors for suppressing a background of low energy levels and maintaining true photo-peaks. Finally, we analyzed pre-processed data and detected photo-peaks using PCA. We measured Cesium 137(Cs-137) and Barium 133(Ba-133) with 1 and 10 micro curies collected from the various distance. Each spectrum was collected for a second and total 60 sets were stored for each isotope. Results of our research show that the proposed algorithm achieves high sensitivity and specificity, proving that the algorithm is appropriate for RM systems.