Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010, Article ID 706732, 10 pages doi:10.1155/2010/706732 Research Article Geometrical Feature Extraction from Ultrasonic Time Frequency Responses: An Application to Nondestructive Testing of Materials Soledad G ´ omez, 1 Ram ´ on Miralles (EURASIP Member), 1 Valery Naranjo, 2 and Ignacio Bosch 1 1 Departamento de Comunicaciones, Instituto de Telecomunicaciones y Aplicaciones Multimedia (iTEAM), Universidad Polit´ ecnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain 2 Instituto de Bioingenier´ ıa y Tecnolog´ ıa Orientada al Ser Humano, Universidad Polit´ ecnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain Correspondence should be addressed to Ram ´ on Miralles, rmiralle@dcom.upv.es Received 30 December 2009; Revised 1 March 2010; Accepted 17 March 2010 Academic Editor: Jo˜ ao Manuel R. S. Tavares Copyright © 2010 Soledad G ´ omez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction Signal processing is an essential tool in nondestructive mate- rial characterization. Modern technologies can take benefit of more sophisticated algorithms allowing to classify and characterize materials precisely. One of the techniques that takes advantage of all these advances is the nondestructive testing (NDT) using ultrasounds. Thanks to the advances in signal processing it is now easy to find applications of NDT using ultrasonics in materials, that some years ago was very hard to find [1–3]. The Signal Processing Group (GTS) of the Universidad Polit´ ecnica de Valencia published a technique [2] that allows to characterize dispersive materials by means of pulse- echo inspection with ultrasonic energy. The aforementioned technique was based on extracting time of flight-dependent parameters from the ultrasonic A-scan. This technique involves assuming a Linear Time Varying (LTV) model for the ultrasonic inspection of dispersive material. The extracted parameters were affected by the physical properties of the material and automatic classifiers could be used. In this paper we introduce a novel technique to extract parameters, based on the shape analysis of time frequency responses, that complement or in some situations improve the performance of the previously published methods. This work is going to be structured as follows. In Section 2 we describe a simple model that demonstrates how physical properties of scattering materials affect the time frequency representation (TFR) of the A-scan. Later, in Section 3, we briefly review the traditional parameter estimators presented in [2]. In Section 4 a new technique based on computing geometrical descriptors from the TFR is introduced. A comparative study of the traditional versus the new proposed technique is presented in Section 5. An