An Anisotropic Regularized Skew Normal Mixture Model for 3D-MRI Brain Tissue Segmentation

Brain structure segmentation from magnetic resonance images is important for clinical diagnosis. Traditional symmetric generative models segment tissues isotropically, limiting their ability to capture anisotropic structures such as curvilinear or linear regions. To address this issue, we propose an anisotropic regularized skew normal mixture model for automatic brain tissue segmentation. A hybrid diffusion tensor is employed within a Markov Random Field to construct anisotropic regularization and suppress imaging noise, while the skew normal mixture model approximates the log-likelihood for improved adaptability. Model parameters are learned using the Expectation–Maximization algorithm. Experiments on synthetic and clinical datasets demonstrate that the proposed method achieves competitive performance compared with state-of-the-art approaches.