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
1999-01-01
123482 byte
Conference paper
Electronic Resource
English
Automatic Correction of Bias Field in Magnetic Resonance Images
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