Lin J S, Cheng K S, Mao C W
Department of Electrical Engineering, National Cheng Kung University, Tainan.Taiwan, ROC.
Int J Biomed Comput. 1996 Aug;42(3):205-14. doi: 10.1016/0020-7101(96)01199-3.
This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.
本文展示了一种用于分割多光谱磁共振脑图像的模糊霍普菲尔德神经网络。所提出的方法是一种基于模糊聚类技术的新型无监督二维霍普菲尔德神经网络。其实现包括二维霍普菲尔德神经网络与模糊c均值聚类算法的结合，以便使多光谱磁共振脑图像分割的并行实现可行。为了产生可行的结果，霍普菲尔德神经网络中包含模糊c均值聚类策略，以消除在基于模式分类中常用的一个基本概念（称为“类内散度矩阵”原理）制定的能量函数中寻找加权因子的需求。所建议的模糊c均值聚类策略也已被证明是收敛的，并且与传统霍普菲尔德神经网络相比，能使网络学习更有效。实验结果表明，基于类内散度矩阵的模糊霍普菲尔德神经网络可以获得接近最优的解。
