PATCH BASED INTENSITY NORMALIZATION OF BRAIN MR IMAGES

Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tis-sue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus ne-cessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations–which try to find a global one-to-one inten-sity mapping based on histograms–we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based meth-ods when normalizing phantoms simulated with various pa-rameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization. Index Terms — MRI, intensity normalization, intensity standardization, brain, segmentation