3D Method of Using Spatial-Varying Gaussian Mixture and Local Information to Segment MR Brain Volumes
The paper is an extension of previous work on spatial-varying Gaussian mixture and Markov random field (SVGM-MRF) from 2D to 3D to segment the MR brain volume with the presence of noise and inhomogeneity. The reason for this extension is that MR brain data are naturally three dimensional, and the information from the additional dimension provides a more accurate conditional probability representation. To reduce large computation time and memory requirements for 3D implementation, a method of using only the local window information to perform the necessary parameter estimations and to achieve the tissue labeling is proposed. The experiments on fifteen brain volumes with various noise and inhomogeneity levels and comparisons with other three well-known 2D methods are provided. The new method outperforms all three 2D methods for high noise and inhomogeneity data which is a very common occurrence in MR applications.
KeywordsWhite Matter Gray Matter Cerebral Spinal Fluid Markov Random Field Local Window
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