Identifying constituent spectra sources in multispectral images to quantify and locate cervical neoplasia

摘要

Optical spectroscopy has been shown to be an effective method for detecting neoplasia. Guided Therapeutics has developed LightTouch, a non invasive device that uses a combination of reflectance and fluorescence spectroscopy for identifying early cancer of the human cervix. The combination of the multispectral information from the two spectroscopic modalities has been shown to be an effective method to screen for cervical cancer. There has however been a relative paucity of work in identifying the individual spectral components that contribute to the measured fluorescence and reflectance spectra. This work aims to identify the constituent source spectra and their concentrations. We used non-negative matrix factorization (NNMF) numerical methods to decompose the mixed multispectral data into the constituent spectra and their corresponding concentrations. NNMF is an iterative approach that factorizes the measured data into non-negative factors. The factors are chosen to minimize the root-mean-squared residual error. NNMF has shown promise for feature extraction and identification in the fields of text mining and spectral data analysis. Since both the constituent source spectra and their corresponding concentrations are assumed to be non-negative by nature NNMF is a reasonable approach to deconvolve the measured multispectral data. Supervised learning methods were then used to determine which of the constituent spectra sources best predict the amount of neoplasia. The constituent spectra sources found to best predict neoplasia were then compared with spectra of known biological chromophores.