基于互信息的弹性图像配准是医学图像配准的重要方法之一。然而由于互信息在小样本图像配准中，会出现多局部极值和极值偏离问题，从而容易出现配准误差，进而造成整图的弹性配准误差。为减少这种配准误差，提出了一种基于特征分类的互信息医学图像弹性配准方法。该方法先采用图像的灰度和梯度特征训练自组织映射（self-organized mapping，SOM）神经网络特征分类器，将图像由高维灰度空间映射到低维特征类别空间；然后，在特征类别空间进行互信息图像弹性配准。实验结果表明，该方法大大提高了小样本图像配准的成功率，并可通用于有噪和无噪的医学图像弹性配准中。

Mutual information registration based on pixel intensity has been widely used in recent years. However, its application to subimages with small samples is questionable, because many localmaximums may happen or the global maximum may be away from the actualmaximum value which causes unnecessary registration error. A new approach, mutual information registration based on feature label(MIF), is proposed to solve such problem. This method first uses image’s intensity and gradient features to train the selforganized mapping(SOM) neural network, and then builds up the feature classifier for each modal image. Using such a classifier, images are project into a feature space with decreased dimensions. Finallymutual information is evaluated in the feature space tomatch images. Our results demonstrate that this method increases the success rate of the subimage registration, and is optimal for thewhole images(eitherwith or without noise) elastic registration.