Principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis of colonic mucosal tissue fluorescence spectra

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Objective: The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Background Data: Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. Methods: A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. Results: The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. Conclusion: The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.

Original languageEnglish
Pages (from-to)659-668
Number of pages10
JournalPhotomedicine and Laser Surgery
Volume27
Issue number4
DOIs
Publication statusPublished - 01-08-2009

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Principal Component Analysis
Principal component analysis
Mucous Membrane
Fluorescence
Colonic Diseases
Tissue
Sensitivity and Specificity
Laser excitation
Pulsed lasers
Spectrum Analysis
Lasers
Spectroscopy
Neural networks

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

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title = "Principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis of colonic mucosal tissue fluorescence spectra",
abstract = "Objective: The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Background Data: Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. Methods: A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. Results: The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100{\%} and 91.3{\%}, respectively. Conclusion: The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.",
author = "Kamath, {Sudha D.} and Mahato, {Krishna K.}",
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N2 - Objective: The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Background Data: Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. Methods: A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. Results: The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. Conclusion: The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.

AB - Objective: The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Background Data: Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. Methods: A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. Results: The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. Conclusion: The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.

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