Automated weak signal extraction of hyperspectral Raman imaging data by adaptive low‐rank matrix approximation. (2nd November 2020) - He, Hao; Lin, Chun; Zong, Cheng; Xu, Mengxi; Zheng, Peng; Ye, Ruiqian; Wang, Lei; Ren, Bin
Abstract: Hyperspectral Raman imaging has emerged as a promising spectroscopic tool that can provide spatial and molecular information of the sample in a label‐free and noninvasive manner, which is very suitable to the biological and biomedical research. However, the intrinsically weak Raman scattering effect results in the low signal quality of the measured Raman spectra, which has largely limited the application of Raman imaging. In this paper, we develop an adaptive low‐rank matrix approximation method to automatically extract the signal from the noisy hyperspectral Raman imaging data. After spike removal, the hyperspectral Raman imaging data are decomposed into a linear combination of submatrices by singular value decomposition. Next, the submatrices are classified into positive and negative groups according to the SNR contribution. The negative group, reflecting the instrumental noise, is discarded, and the positive group is used to reconstruct the denoised signal. We prove on the simulated data that this algorithm can significantly decrease the normalized mean noise error from 34.35% down to 1.9%. Such a strong denoising performance enables it to efficiently extract the signal from the noisy hyperspectral Raman imaging data, especially under the low SNR condition. We finally apply this algorithm to the fast speed Raman imaging of HeLa cell and surprisingly find that the slight difference of the spectra can be differentiated after signal extraction. This algorithm offers a promising tool in the Raman imaging and also can be extended to other spectroscopic imaging platforms such as fluorescent and IR spectroscopy. Abstract : An adaptive low‐rank matrix approximation (ALRMA) algorithm is proposed to automatically extract the signal for the hyperspectral Raman imaging (HRI) data. The ALRMA first factorize the HRI data into a series of submatrices by singular value decomposition, and then two phases (truncation and selection) are employed to extract the signal. A SNR criterion has been proposed to classify the submatrices into positive and negative contribution groups to indicate how to choose proper submatrices to reconstruct the denoised signal.