Segmentation of Brain MR Images with Bias Field Correction.
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We consider a statistical model-based approach to the segmentation of magnetic resonance (MR) images with bias field correction. The proposed method of penalized maximum likelihood is implemented via the expectationconditional maximization (ECM) algorithm, using an approximation to the E-step based on a fractional weight version of the iterated conditional modes (ICM) algorithm. A Markov random field (MRF) is adopted to model the spatial dependence between neighouring voxels. The approach is illustrated using some simulated and real MR data.
Proceedings of the 2003 APRS Workshop on Digital Image Computing (WDIC 2003)