Target-to-background separation for spectral unmixing in in-vivo fluorescence imaging

We present a novel fluorescence spectral unmixing based on target-to-background separation pre- processing, which effectively separates the multi-target fluorescence from all background autofluorescence （BF） without any hardware-based BF acquisition and tissue specific BF estimation. Specifically, we first enhance the intrinsic accumulation contrast in target-to-background fluorescence using h-dome transformation; then separate multi-target fluorescence areas from the background in sparse multispectral data utilizing kernel maximum auto- correlation factor analysis; we further use fast marching-based image inpainting method to patch up the removed target fluorescence areas and reconstruct the multispectral BF; with the BF matrix being subtracted from the original data, the multi-target fluorophores are easily unmixed from the subtracted data using multivariate curve resolution-alternating least squares method. In two preliminary in-vivo experiments, the proposed method demon- strated excellent performance to unmix multi-target fluorescences while other state-of-art unmixing methods failed to get desired results.