Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)
Erik Learned-miller, Parvez Ahammad
The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase the likelihood of the pix- els in a single image by adaptively estimating a correction to the unknown image bias field. The pixel likelihoods are defined either in terms of a pre-existing tissue model, or non-parametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the im- age is not used to calculate the likelihoods. We suggest a new approach in which we simultaneously eliminate the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different images, rather than within an image, to eliminate bias fields from all of the images simultaneously. The method builds a "multi-resolution" non-parametric tissue model conditioned on image location while eliminating the bias fields associated with the orig- inal image set. We present experiments on both synthetic and real MR data sets, and present comparisons with other methods.