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, w...

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Published inShanghai jiao tong da xue xue bao Vol. 19; no. 5; pp. 600 - 611
Main Author 赵勇 胡程 彭金良 秦斌杰
Format Journal Article
LanguageEnglish
Published Heidelberg Shanghai Jiaotong University Press 2014
Subjects
Architecture
Autocorrelation
Chemical compounds
Computer Science
Electrical Engineering
Engineering
Fluorescence
Image enhancement
Kernels
Least squares method
Life Sciences
Materials Science
Separation
Spectra
Transformations
autofluorescence removal
target detection
Q 632
spectral unmixing
target-to-background separation
fluorescence imaging
kernel maximum autocorrelation factor
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Summary: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.
Bibliography:fluorescence imaging, spectral unmixing, autofluorescence removal, target detection, kernel maxi-mum autocorrelation factor, target-to-background separation
31-1943/U
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.
ZHAO Yong , HU Cheng , PENG Jin-liang, QIN Bin-jie （School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200240, China）
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-014-1548-9