The need for robust defect detection in NDE applications requires the identification of subtle, low-contrast changes in measurement signals usually in very noisy data. Most algorithms rarely perform at the level of a human inspector and often, as data sets are now routinely 10+ Gigabytes, require laborious manual inspection. We present two automated defect segmentation methods, simple threshold and a binomial hypothesis test, and compare effectiveness of these approaches in noisy data with signal to noise ratios at 1:1. The defect-detection ability of our algorithm will be demonstrated on a 3D CT volume, UT C-scan data, magnetic particle images, and using simulated data generated by XRSIM. The latter is a physics-based forward model useful in demonstrating the effectiveness of data processing approaches in a simulation which includes complex defect geometry and realistic measurement. These large data setsrepresent significant demands on compute resources and easily overwhelm typical PC platforms; however, the emergence of graphics processing units(GPU) processing power provides a means to overcome this bottleneck. Processing large, multi-dimensional datasets requires an optimal GPU implementation which addresses both computational complexity and memory-bandwidth usage.