We present a novel fluorescence spectral unmixing based on target-to-background separation preprocessing, which effectively separates the multi-target fluorescence from all background autofluorescence(BF)without any h...We present a novel fluorescence spectral unmixing based on target-to-background separation preprocessing, 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 autocorrelation 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 demonstrated excellent performance to unmix multi-target fluorescences while other state-of-art unmixing methods failed to get desired results.展开更多
基金the Small Animal Imaging Project supported by Geneway Biotech International Trading Co.,Ltd.(No.06-545)the National Natural Science Foundation of China(Nos.61271320,60872102 and 60402021)
文摘We present a novel fluorescence spectral unmixing based on target-to-background separation preprocessing, 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 autocorrelation 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 demonstrated excellent performance to unmix multi-target fluorescences while other state-of-art unmixing methods failed to get desired results.
