Kernel morphological differences algorithm based on informative energy metric
结合信息能度量的核形态差异学习*

Firstly,this paper defined the similarity measure by kernel mapping and non-local means,named as informative energy metric(IEM).Secondly,combined the feature mapping of cell images with the IEM and an objective function for morphological differences was computed.Thirdly,it obtained the optimal metric matrix by gradient ascent algorithm and constructed a morphological difference learning model based on kernel methods.The characteristics of this model were that it could measure the similarity between each sample pairs and mine the high order statistics and nonlinear features.Experimental results show that this method is more sensitive and robust,it can be used in disease diagnosis.