Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis
Abstract
A novel spectral imaging method for the classification of light-induced autofluorescence spectra based on principal component analysis (PCA), a multivariate statistical analysis technique commonly used for studying the statistical characteristics of spectral data, is proposed and investigated. A set of optical spectral filters related to the diagnostically relevant principal components is proposed to process autofluorescence signals optically and generate principal component score images of the examined tissue simultaneously. A diagnostic image is then formed on the basis of an algorithm that relates the principal component scores to tissue pathology. With autofluorescence spectral data collected from nasopharyngeal tissue in vivo, a set of principal component filters was designed to process the autofluorescence signal, and the PCA-based diagnostic algorithms were developed to classify the spectral signal. Simulation results demonstrate that the proposed spectral imaging system can differentiate carcinoma lesions from normal tissue with a sensitivity of 95% and specificity of 93%. The optimal design of principal filters and the optimal selection of PCA-based algorithms were investigated to improve the diagnostic accuracy. The robustness of the spectral imaging method against noise in the autofluorescence signal was studied as well.
- Publication:
-
Journal of the Optical Society of America A
- Pub Date:
- September 2002
- DOI:
- 10.1364/JOSAA.19.001823
- Bibcode:
- 2002JOSAA..19.1823Q
- Keywords:
-
- Cancer;
- Diseases;
- Emission Spectra;
- Fluorescence;
- Imaging Techniques;
- Multivariate Statistical Analysis;
- Principal Components Analysis;
- Spectrum Analysis;
- Tissues (Biology);
- Instrumentation and Photography