Department of Signal Theory, Networking and Communications, University of Granada, Spain.
Comput Biol Med. 2013 Jun;43(5):559-67. doi: 10.1016/j.compbiomed.2013.01.003. Epub 2013 Feb 26.
This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of α-stable distributions. A Bayesian α-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The α-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the α-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.
本研究探讨了脑磁共振成像（MRI）中灰质（GM）和白质（WM）的分布。使用α-稳定分布混合模型来描述 GM 和 WM 的分布。提出了一种用于直方图数据的贝叶斯α-稳定混合模型，并使用 Metropolis-Hastings 算法对未知参数进行采样。该方法在来自 MRI 脑分割库的 18 个真实图像上进行了测试。准确估计了 GM 和 WM 的分布。本文提出的α-稳定分布混合模型可作为使用空间信息的更复杂 MRI 分割过程的前置步骤。此外，由于α-稳定分布是高斯分布的推广，因此本文提出的方法可以替代在文献中广泛用于脑 MRI 分割的高斯混合模型。
