  Nondestructive evaluation (NDE) techniques are widely used to detect flaws in
critical components of systems like aircraft engines, nuclear power plants and
oil pipelines in order to prevent catastrophic events. Many modern NDE systems
generate image data. In some applications an experienced inspector performs the
tedious task of visually examining every image to provide accurate conclusions
about the existence of flaws. This approach is labor-intensive and can cause
misses due to operator ennui. Automated evaluation methods seek to eliminate
human-factors variability and improve throughput. Simple methods based on peak
amplitude in an image are sometimes employed and a trained-operator-controlled
refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR)
has also been implemented. We develop an automated and optimized detection
procedure that mimics these operations. The primary goal of our methodology is
to reduce the number of images requiring expert visual evaluation by filtering
out images that are overwhelmingly definitive on the existence or absence of a
flaw. We use an appropriate model for the observed values of the SNR-detection
criterion to estimate the probability of detection. Our methodology outperforms
current methods in terms of its ability to detect flaws.
