Reducing the dimensionality of feature vector in quantitative cytological analysis
In order to improve the performance and efficiency in quantitative cytological analysis systam,a method to reduce the dimensionality of cell nuclei feature vector was proposed.First,the statistically based F-score value of each cell nucleus feature was selected and apparent useless features were rejected.Then,the RF algorithm was conducted on the remaining features,and they were sorted in descending order by RF-score value.After evaluating the performances of the cell nuclei classifiers under the conditions of different numbers of features,the final feature vector for cell nuelei recognition was determined.Experiment results show that compared with the original cell nuclei classifier,the dimensionality reduction algorithm can save about 5 0% computation time in the final classitier, and raise the cell nuclei recognition accuracy from 91.32% to 98.67 %.
国家科技期刊平台
NSTL
万方数据
维普
