基于双向2DPCA和SVM的胃粘膜肿瘤细胞识别  

GASTRIC MUCOSA TUMOUR CELLS RECOGNITION BASED ON BIDIRECTIONAL 2DPCA AND SVM

作　　者：甘岚[1] 谢丽娟[1] 

机构地区：[1]华东交通大学信息工程学院,江西南昌330013

出　　处：《计算机应用与软件》2015年第2期155-158,共4页Computer Applications and Software

基　　金：国家自然科学基金项目(61163040)

摘　　要：针对胃粘膜肿瘤细胞图像的高维性、不规则性及复杂性特征,提出基于双向2DPCA(二维主成分分析)和SVM(支持向量机)的肿瘤细胞识别方法。双向2DPCA同时对图像行、列方向进行特征提取运算,大大降低图像特征维数。结合基于统计理论的SVM在分类识别方面的优势,通过引入核函数巧妙地解决非线性问题,从而快速有效地实现细胞分类。实验表明该方法不但有效提高了识别率,而且算法时间明显减少。We propose a tumour cells recognition algorithm for gastric mucosa, it is based on Bi2DPCA （bidirectional 2 dimensional principal component analysis） and SVM （support vector machine） and aims at the features of high dimensionality, irregularity and complexity the tumour ceils＇ image has. Bidirectional 2DPCA operates the feature extraction on both row and column direction of the image simultaneously, so greatly reduces the feature dimensions of image. Combining the advantage of statistical theory-based SVM in classification and recognition, the method skilfully solves the nonlinear problem by introducing kernel function, so that quickly and efficiently realises cells classification. Experimental results show that the proposed method can improve the classification rates, and the time of algorithm is decreased significantly as well.

关 键 词：双向2DPCA SVM 特征提取 核函数 非线性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]