title: Normalization of brain magnetic resonance images using histogram even-order derivative analysis.
abstract: The even-ordered (2nd, 4th and 6th) derivatives of a brain MRI histogram were used to calculate a characteristic value for white matter, which was used to normalize the image intensity scale. Simulated image histograms were used to estimate the methodological error as a function of noise level, and the optimum derivative order was determined for each image type studied (T1-, T2- and density-weighted). The algorithm yielded highly reproducible results when used in conjunction with a threshold-sensitive brain segmentation algorithm. It also proved insensitive to the presence of extra-cranial tissues. This method of histogram analysis could find utility in a variety of applications that demand robust intensity normalization including image registration, brain segmentation, tissue classification and spatial inhomogeneity correction.
authors.affiliation: University of Louisville, School of Medicine, Departments of Psychiatry and Behavioral Sciences and Radiology, Louisville, KY 40292, USA. james.christensen@louisville.edu
authors.forename: James D
authors.initials: JD
authors.lastname: Christensen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods