国防科技大学学报2017，Vol.39Issue(6)：45-50,6.DOI:10.11887/j.cn.201706008
结合层次法与主成分分析特征变换的宫颈细胞识别
Cervical cell recognition based on hierarchical method and principal component analysis feature transformation
摘要
Abstract
In order to recognize multi-class cervical cells automatically,a hierarchical method with PCA ( principal component analysis) feature transformation was proposed and this cell recognition could provide the evidence for cervical cancer diagnosis.The cervical cell recognition was treated as a 4-class classification problem.There were two levels in this hierarchical method.First,one-versus-one strategy was used to train 6 SVM ( support vector machine) classifiers to do a 3-class classification.Second,abnormal cells in one type of 3 categories were classified by a 2-class SVM.To optimize the feature combination and reduce the running time,a feature transformation method named PCA was adopted to transform the original feature vector into low-dimension feature space.The experiments show that the proposed hierarchical PCA recognition method is faster than the common hierarchical method at a ratio of 21.31％,and can distinguish 4 cervical cell categories better than 6 other traditional methods and achieve above 90％ accuracy.关键词
宫颈涂片图像/特征变换/层次多分类/宫颈细胞识别Key words
cervical smear image/feature transformation/hierarchical multi-class classification/cervical cell recognition分类
信息技术与安全科学引用本文复制引用
赵理莉,孙燎原,殷建平,李宽,印万鹏,祝恩..结合层次法与主成分分析特征变换的宫颈细胞识别[J].国防科技大学学报,2017,39(6):45-50,6.基金项目
国家自然科学基金资助项目(61170287,61232016,61303189,61672528) （61170287,61232016,61303189,61672528）
