Geometrical Feature Extraction from Ultrasonic Time Frequency Responses: An Application to Nondestructive Testing of Materials. (28th April 2010)

• Record Type: Journal Article
• Authors: Gómez Gómez, Soledad Soledad; Miralles Miralles, Ramón Ramón; Naranjo Naranjo, Valery Valery; Bosch Bosch, Ignacio Ignacio
• Other Names: Tavares Tavares João Manuel R. S. João Manuel R. S. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-04-28
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/706732
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : Signal processing is an essential tool in nondestructive material characterization. Pulse-echo inspection with ultrasonic energy provides signals (A-scans) that can be processed in order to obtain parameters which are related to physical properties of inspected materials. Conventional techniques are based on the use of a short-term frequency analysis of the A-scan, obtaining a time-frequency response (TFR), to isolate the evolution of the different frequency-dependent parameters. The application of geometrical estimators to TFRs provides an innovative way to complement conventional techniques based on the one-dimensional evolution of an A-scan extracted parameter (central or centroid frequency, bandwidth, etc.). This technique also provides an alternative method of obtaining similar meaning and less variance estimators. A comparative study of conventional versus new proposed techniques is presented in this paper. The comparative study shows that working with binarized TFRs and the use of shape descriptors provide estimates with lower bias and variance than conventional techniques. Real scattering materials, with different scatterer sizes, have been measured in order to demonstrate the usefulness of the proposed estimators to distinguish among scattering soft tissues. Superior results, using the proposed estimators in real measures, were obtained when classifying according to mean scatterer size.