Abstract: 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.

Key words: laser-induced fluorescence spectra, feature extraction, Principal Component Analysis（PCA）, Fisher Linear Discriminative Analysis（FLDA）, Support Vector Machine（SVM）

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%。

关键词: 激光诱导荧光光谱, 特征提取, PCA, FLDA, 支持向量机