Wavelet Based Identification of Substances in Terahertz Tomography Measurements

In comparison to the X-ray computed tomography Terahertz technique significantly enhances the amount of the information acquired during the sample measurement. Not only amplitude, but also phase, time and spectral characteristics can be determined in THz time-domain spectroscopy. Thus, Terahertz tomography allows localization and identification of substances within the objects due to the characteristic fingerprints in this frequency range. Certainly, an appropriate data processing and comparison algorithms are crucial for the accurate identification of the substances in the measured sample. Therefore, we present a new wavelet-based identification method which is suitable even for the substances with broad absorption curves and small or no absorption peaks. The performance of this algorithm was evaluated with the help of a tomographic sample filled with four substances, which were previously characterized for the external database. The continuous wavelet transform was applied to every data cell of the tomographic measurement and compared to the database. Received sinograms were reconstructed into images which depict estimated similarity between the measured and database substances. Furthermore, we suggest a method for the reduction of spectral data after the continuous wavelet transform. This method is based on the extraction of the distinctive features in the form of ridge lines