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Abstract #3532

Utility of Stockwell Transform variants for local feature extraction from MR images: evidence from multiple sclerosis lesions

Glen Pridham1, Olayinka Oladosu2, and Yunyan Zhang1
1Department of Radiology, Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 2Department of Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

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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