Probability-based differential normalized fluorescence bivariate analysis for the classification of tissue autofluorescence spectra

Differential normalized fluorescence (DNF) is an efficient and effective method for the differentiation of normal and cancerous tissue fluorescence spectra. The diagnostic features are extracted from the difference between the averaged cancerous and averaged normal tissue spectra and used as indices in tissue classification. In this paper, a new method, probability-based DNF bivariate analysis, is introduced based on the univariate DNF method. Two differentiation features are used concurrently in the new method to achieve better classification accuracy. The probability of each sample belonging to a disease state is determined with Bayes decision theory. This probability approach classifies the tissue spectra according to disease states and provides uncertainty information on classification. With a data set of 57 colonic tissue sites, probability-based DNF bivariate analysis is demonstrated to improve the accuracy of cancer diagnosis. The bivariate DNF analysis only requires the collection of a few data points across the entire emission spectrum and has the potential of improving data acquisition speed in tissue imaging.