MR images are usually corrupted by a smoothly varying artefact that modulates the intensity of the image. This poster presents a method for removing most of this artefact.
Most current methods are either:
Parametric: Bias correction is incorporated into a mixture of Gaussians type approach, possibly as a refinement of a tissue classification algorithm.
Bias correction component is normally applied to log-transformed intensities (e.g. Wells III et al, 1996; Van Leemput et al, 1999).
Non-parametric: Bias correction applied to histograms of intensities in order to maximise entropy.
Most widely used approach is applied to histograms of log-transformed intensities (Sled et al, 1998).
Another approach minimises entropy of the histogram of the original intensities, with a modification to preserve the average intensity in the image (Mangin, 2000).
