Song Tao, Jamshidi Mo M, Lee Roland R, Huang Mingxiong
Man Radiology Department, University of California at San Diego, San Diego, CA 92103, USA.
IEEE Trans Neural Netw. 2007 Sep;18(5):1424-32. doi: 10.1109/tnn.2007.891635.
A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated.
提出了一种用于磁共振成像（MRI）脑组织分割的改进概率神经网络（PNN）。在这种方法中，使用协方差矩阵代替PNN核函数中的奇异平滑因子，并在求和层模式中添加加权因子。这种加权概率神经网络（WPNN）分类器不仅可以在最终结果阶段，还可以在建模过程中考虑MRI中常见的部分容积效应。它采用自组织映射（SOM）神经网络对输入的MR图像进行过度分割，并生成概率密度函数（pdf）估计所需的参考向量。开发了一种基于贝叶斯规则的监督“软”标记机制，以便可以与相应的SOM参考向量一起生成加权因子。比较了各种算法的组织分类结果，并证明了所提方法的有效性和鲁棒性。
