Dept. of Radiol., Case Western Reserve Univ., Cleveland, OH.
IEEE Trans Med Imaging. 1992;11(2):215-20. doi: 10.1109/42.141645.
The application of the Hopfield neural network for the multispectral unsupervised classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp classification map using proton density-weighted and T(2)-weighted images in the head. The preliminary studies indicate that the number of iterations needed to reach ;good' solutions was nearly constant with the number of clusters chosen for the problem.
报告了 Hopfield 神经网络在磁共振（MR）图像多光谱无监督分类中的应用。在头部，使用质子密度加权和 T2 加权图像的胜者全取神经元获得清晰的分类图。初步研究表明，达到“良好”解所需的迭代次数与为问题选择的聚类数几乎呈常数关系。
