Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis.
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.
Qu
Jianan Y
JY
Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Kowloon, China. eequ@ust.hk
Chang
Hanpeng
H
Xiong
Shengming
S
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D009303:Nasopharyngeal Neoplasms / Q000175:diagnosis*; D025341:Principal Component Analysis; D013050:Spectrometry, Fluorescence