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Previous work has demonstrated the advantages of Shannon entropy (H) analysis for the image-based detection of defects in both plexiglas and graphite/epoxy composites [1][2][3]. Application to experimental data shows that the analysis is fast and robust in the presence of noise. However, it suffers from the shortcoming that when signal averaging is employed, H converges to a constant, independent of the underlying waveform characteristics (log2(Ns), where Ns is the number of gated time domain sample points). By considering a generalization of the Shannon entropy to the continuous waveform case, H c, we eliminate this problem and obtain a stable numerical scheme for evaluation of Hc based on the use of Fourier series. As described previously, however, this approach requires a network of 20 workstations over 20 hours to complete analysis of one 41 by 201 pixel image. We describe a new approach for calculating continuous waveform entropy Hc, based on the use of a Green's function. We show that the new approach produces the same or higher image contrast in a time that is roughly three orders of magnitude smaller than that required by the Fourier series method. This improvement arises from two sources. The Green's function approach has greater inherent immunity to noise, and requires fewer calculations than the Fourier series approach. The resulting algorithm makes it feasible to perform Hc analysis on a personal computer