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Segmentation of Brain MR Images with Bias Field Correction
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Deming Mclachlan, Geoffrey J. Kim, Seung-Gu Ng, Shu-Kay |
| Abstract | 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Bias Field Correction Markov Random Field Statistical Model-based Approach Magnetic Resonance Real Mr Data Spatial Dependence Fractional Weight Version Iterated Conditional Mode Brain Mr Image Penalized Maximum Likelihood Expectationconditional Maximization |
| Content Type | Text |