Title: Feature analysis and denoising of MRS data based on pattern recognition and wavelet transform

Authors: Guangbo Dong, Jian Ma, Guihai Xie, Zengqi Sun

Addresses: 2862# Institute, Air Force Equipment Academy, 100085, Beijing, China. ' Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China. ' Department of Computer Engineering, Ordnance Engineering College, 050003, Shijiazhuang, China. ' Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China

Abstract: Denoising the MRS data to provide better data sources and feature analysis of spectroscopy are the main concerns in MRS data processing. This paper describes an effective method based on wavelet transformation and pattern recognition technologies. According to the characteristics of MRS data, a new wavelet base function was designed, and denoising of FID data was performed by using wavelet threshold to obtain better MRS spectra firstly, then extracted the feature of certain cancers from MRS spectra based on independent component analysis (ICA) and support vector machine (SVM). Contrast with the denoising effect of conventional wavelet base functions, the experimental results confirmed the validity of the feature extraction method of ICA, and the newly-designed wavelet filter set showed better performance. Experiments were carried out on small amounts of very low SNR datasets which were obtained from the GE NMR device, and the results showed the improved effect on denoising and feature extraction.

Keywords: biomedical signal processing; wavelet transform; pattern recognition; nuclear magnetic resonance; NMR; MRS denoising; magnetic resonance spectroscopy; feature analysis; cancer; independent component analysis; ICA; support vector machines; SVM; feature extraction; signal-to-noise ratio.

DOI: 10.1504/IJCSE.2011.042016

International Journal of Computational Science and Engineering, 2011 Vol.6 No.3, pp.141 - 145

Published online: 18 Mar 2015 *

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