The Stockwell Transform (ST) is an advanced local spectral feature estimator, that is prohibitively large for use in machine learning applications for typical MR images. We compared two memory-efficient variants: the Polar ST (PST) and the Discrete Orthogonal ST (DOST) as feature extraction steps in competing random forest classifiers, built to classify white matter regions-of-interest as: lesion or normal-appearing. The DOST failed to out-perform guessing, whereas the PST: out-performed guessing, and improved the accuracy of an intensity-based random forest, achieving 88.8% accuracy. We conclude that the PST can complement MR intensity, whereas the DOST may not.
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