Automated classification of crystallization experiments using wavelets and statistical texture characterization techniques

A method is presented for the classification of protein crystallization images based on image decomposition using the wavelet transform. The distribution of wavelet coefficient values in each sub-band image is modelled by a generalized Gaussian distribution to provide discriminatory variables. These statistical descriptors, together with second-order statistics obtained from joint probability distributions, are used with learning vector quantization to classify protein crystallization images.

提出了一种基于小波变换的图像分解对蛋白质结晶图像进行分类的方法。通过广义高斯分布对每个子带图像中的小波系数值分布进行建模，以提供可区分的变量。这些统计描述符以及从联合概率分布中获得的二阶统计量，与学习矢量量化一起用于对蛋白质结晶图像进行分类。