Optical pathology using oral tissue fluorescence spectra: classification by principal component analysis and k-means nearest neighbor analysis

Abstract

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.

Spatial Laser Induced Fluorescence Imaging (SLIFIMG) Analysis to Assess Tissue Photosensitizer Uptake

“…Korbelik, M. 1993, stated that the most sensitive means of monitoring photosensitizer photodegradation is fluorescence detection and it is therefore important as far as possible to study photodegradation of photosensitizer in tumour tissues under treatment with PDT compared to normal tissues so that a cleaner correlation between photodegradation (fluorescence decreasing) and photodamage may be made [22]. In addition Ochsner, M. 1997 mentioned that the understanding of the pattern of the photosensitizer fluorescence -photobleaching -is important for the development of monitoring photodynamic therapy efficacy [23]. Pogue, B.W.…”

Photoacoustic spectroscopy of ovarian normal, benign, and malignant tissues: a pilot study

“…6,8,9 In this method, an unknown sample is classified to that class having the most "similar" or "nearest" sample point in the training set of data. In the present study, for classification of ovarian data, the single nearest neighbor method has been used.…”

“…Laser-induced spectroscopy techniques, [4][5][6][7][8][9][10][11][12] such as fluorescence, [4][5][6][7][8][9][10] Raman, 11 and photoacoustic 12 (PA), have been widely investigated as nondestructive optical tools for medical diagnostics. [4][5][6][7][8][9][10][11][12][13] Pulsed photoacoustic spectroscopy is one of the blossoming fields of science and, in recent years, the technique has become increasingly popular as a tool for biomedical imaging and detection. 13,14 The technique is based on the detection of an acoustic signal induced by photons and is different than the optical techniques detecting optical signals.…”

“…With this idea, in our laboratory, we have recorded the photoacoustic spectra of normal as well as different stages of ovarian cancer tissue samples in vitro and principal component analysis (PCA) (Refs. 7-10 and 32) based k-nearest neighbor (k-NN), 6,8,9 artificial neural network (ANN), 7,10 and support vector machine (SVM) (Refs. 33 and 34) classification algorithms are developed.…”

Optical Biopsy Method for Breast Cancer Diagnosis Based on Artificial Neural Network Classification οf Fluorescence Landscape Data

“…For more than two decades, various optical spectroscopic techniques including fluorescence spectroscopy have been widely explored as diagnostic tools in the discrimination of normal from abnormal tissues in various organ sites such as breast [2,3], colon [4], oral [5], and skin [6]. The endogenous fluorophores, such as nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), collagen, elastin, amino acids, vitamins, lipids and porphyrins, have a significant variation in the concentration in different tissue types.…”