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Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

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Abstract

Peak detection is a crucial step in spectral signal pre-processing. The accurate locations of characteristic peaks are prerequisite for chemical identification. However, measured spectra inevitably contain both noise and baseline signals. These interference signals will generate a series of false peaks, which is a challenge for spectral analyses. For this purpose, a spectral peak detection algorithm named CWT-IS, based on continuous wavelet transform (CWT) and image segmentation (IS), is proposed. First, a wavelet coefficient matrix is obtained by CWT, then the matrix is converted to a gray image and the image is segmented by a machine vision method, an improved version of the Otsu method based on fuzzy mathematical theory. The desired threshold can be determined based on the membership grades of segmentation performance and the features of false peaks are eliminated. The fuzzy Otsu method solves the problem that the traditional Otsu method cannot effectively handle an image with a unimodal histogram, thus, this method can accurately separate peak regions from the CWT coefficient matrix and ridges representing the peaks are complete. This method has been successfully applied to the peak detection of the simulated spectra and the MALDI-TOF spectra. The experimental results show that CWT-IS can effectively eliminate the adverse effect of noise and baseline and make the features of spectral peaks become more obvious, which is conducive to determine characteristic peak positions.

Graphical abstract: Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

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Publication details

The article was received on 21 Sep 2019, accepted on 22 Nov 2019 and first published on 22 Nov 2019


Article type: Paper
DOI: 10.1039/C9AY02052G
Anal. Methods, 2020, Advance Article

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    Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

    G. Yang, J. Dai, X. Liu, M. Chen and X. Wu, Anal. Methods, 2020, Advance Article , DOI: 10.1039/C9AY02052G

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