Optical pathology using oral tissue fluorescence spectra:: classification by principal component analysis and k-means nearest neighbor analysis
Optical pathology using oral tissue fluorescence spectra:: classification by principal component analysis and k-means nearest neighbor analysis
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DOI：
10.1117/1.2437738
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发表时间：
2007-01-01
影响因子：
3.5
通讯作者：
Mahato, K. K.
中科院分区：
文献类型：
--
作者：
Kamath, Sudha D.;Mahato, K. K.
使用基于 MATLAB@R6 的主成分分析 (PCA) 和 k 均值最近邻 (k-NN) 分析分别对同一组光谱数据进行光谱分析和分类，以区分在 325 nm 激发下记录的经病理证明的正常、癌前和恶性口腔组织的脉冲激光诱导自发荧光光谱。从属于正常组、癌前组和恶性组的 60 个训练样本（谱）的每个谱中提取均值、中值、最大强度、能量、谱残差和标准差等 6 个特征，并用于对参考数据库进行 PCA。使用聚类分析建立正常、癌前和恶性样本的标准校准模型。我们证明，使用 PCA 技术可以将长度为 6 的特征向量减少为三个分量。在特征空间上执行 PCA 后，保留包含所有诊断信息的前三个主成分 (PC) 分数，并丢弃仅包含噪声的其余分数。因此，新的特征空间仅使用三个 PC 分数构建，并用作 k-NN 分类的输入数据库。使用此变换后的特征空间，计算正常、癌前和恶性样本的质心，并实现不同类别口腔样本的有效分类。通过计算统计参数特异性、敏感性和准确性对k-NN分类结果进行性能评估，发现它们分别为100%、94.5%和96.17%。 (c) 2007 年光电仪器工程师协会。
The spectral analysis and classification for discrimination of pulsed laser-induced autofluorescence spectra of pathologically certified normal, premalignant, and malignant oral tissues recorded at a 325-nm excitation are carried out using MATLAB@R6-based principal component analysis (PCA) and k-means nearest neighbor (k-NN) analysis separately on the same set of spectral data. Six features such as mean, median, maximum intensity, energy, spectral residuals, and standard deviation are extracted from each spectrum of the 60 training samples (spectra) belonging to the normal, premalignant, and malignant groups and they are used to perform PCA on the reference database. Standard calibration models of normal, premalignant, and malignant samples are made using cluster analysis. We show that a feature vector of length 6 could be reduced to three components using the PCA technique. After performing PCA on the feature space, the first three principal component (PC) scores, which contain all the diagnostic information, are retained and the remaining scores containing only noise are discarded. The new feature space is thus constructed using three PC scores only and is used as input database for the k-NN classification. Using this transformed feature space, the centroids for normal, premalignant, and malignant samples are computed and the efficient classification for different classes of oral samples is achieved. A performance evaluation of k-NN classification results is made by calculating the statistical parameters specificity, sensitivity, and accuracy and they are found to be 100, 94.5, and 96.17%, respectively. (c) 2007 Society of Photo-Optical Instrumentation Engineers.