![]() ![]() Empirical resampling was carried out using the corresponding data vectors, and the theoretical error predictors were thereby checked for slice thicknesses of 1, 3, 9, and 27 mm, with a distance of 45 mm between slice midplanes. We thereby obtained a stack of 183 serial coronal slices of 1 mm thickness encompassing the whole cerebrum. The data were classified using a fuzzy clustering minimum distance algorithm. Method: Our working data set comprised the GM and WM segmentations obtained from a paradigmatic high signal-to-noise ratio 3D spoiled GRASS MR volume data set for a single healthy human subject. Our goal was to check the error prediction formulas by resampling and to determine the minimum number of MR slices required to estimate the volumes of the cerebrum and of the compartments of gray matter (GM) and white matter (WM) with prescribed errors. Purpose: Recent theory has been developed to estimate volume from a systematic sample of tissue slices of a given thickness and to predict the corresponding error. ![]()
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