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.