摘要
利用主成分分析(Principal Component Analysis,PCA)和Fisher线性判别分析(Fisher Linear Discriminative Analysis,FLDA)方法相结合提取特征,提出了一种荧光光谱特征提取新方法--PCA_FLDA。实验证明,新方法提高了激光诱导自体荧光光谱对早期结肠癌的诊断精度。对预处理后的240条荧光光谱,利用PCA_FLDA算法提取了50个特征变量,利用支持向量机将其分为正常组织和癌变组织,分类敏感性、特异性和准确率可分别达到97.5%、95.12%和96.25%。
Combined with Principal Component Analysis （PCA） and Fisher Linear Discriminative Analysis （FLDA） to extract features,a novel fluorescence spectra feature extraction algorithm named PCA_FLDA is presented in this paper.The experiment results show that this method can improve the diagnostic rate of earlier stage colonic cancer with laser-induced fluorescence spectra.After preprocessing the collected 240 spectra,50 feature variants were extracted with PCA_FLDA.By means of the support vector machine,all spectra are classified into two categories as the normal or the abnormal one.The sensitivity,specificity and discriminating accuracy are reached to 97.5%,95.12% and 96.25%,respectively.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第12期238-240,243,共4页
Computer Engineering and Applications
基金
湖南省自然科学基金(the Natural Science Foundation of Hunan Province of China under Grant No.06JJ50143)
关键词
激光诱导荧光光谱
特征提取
PCA
FLDA
支持向量机
laser-induced fluorescence spectra
feature extraction
Principal Component Analysis（PCA）
Fisher Linear Discriminative Analysis（FLDA）
Support Vector Machine（SVM）
