Two fully automatic restoration-segmentation algorithms are proposed for the processing of biased magnetic resonance images. A first approach is based on an expectation-maximization procedure, where the initial conditions for the class distribution parameters and the number of classes are obtained, without any a priori knowledge, from a mode-based analysis of the biased image. A second approach relies completely on the mode-based analysis to update the number of classes and distribution parameters in every iteration. Both methods give accurate results even for overlapping distributions distorted by a gain factor of up to 40%. The possibility of having automatic initial conditions provides an important enhancement to previously reported methods.
Automatic correction of bias field in magnetic resonance images
01.01.1999
123482 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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