Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques\r\naim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially\r\ncorresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities,\r\nwhich not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial\r\nintensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input\r\nand standard images.We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI\r\nand ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot\r\nE-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in\r\nmore than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching\r\nfor the brain white matter with respect to the standard image.
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