Application of two step clustering in pathological image analysis
Miao He · Chinese Journal of Public Health · 2006
Objective To investigate the potential of two step clustering analysis in pathological image analysis.Methods Two step clustering was used to cluster normal,low-grade squamous intraepithelial lesion,high-grade squamous intraepithelial lesion cervical cells with 51 characters.Firstly,the cases were clustered intomany small sub-clusters and then the sub-clusters were clustered into the desired number of clusters.The number of clusters was automatically determined by Bayesian Information Criteria with log-likelihood distance measure.Results Classification accuracy of normal,low-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesion cervical cells was 98.0%,96.1% and 100%,respectively.Conclusion With the high classification accuracy,the two step clustering can measure the variables'significance,which could provide useful information for image analysis.