Feature Analysis and De-noising of MRS Data Based on Pattern Recognition and Wavelet Transform

Abstract

De-noising the MRS data is a key processing in analysis of spectroscopy MRS data. This paper presents an effective method based on wavelet-transform and pattern recognition technologies. Upon the characteristics of MRS data, a new wavelet basis function was designed, and a de-noising method of free induction decay (FID) data using wavelet threshold to obtain better MRS spectrums was conduced; hence, the features of some cancers from MRS spectrums based on independent component analysis (ICA) and support vector machine (SVM) were extended. Comparing with the de-nosing effect using conventional wavelet basis functions, experiments were conducted to validate that the innovative feature extraction method employing ICA and a new wavelet filter set has higher and better performance. Experiments in this study were carried out on a small amount of real and low SNR dataset that obtained from the GE NMR device. The experimental results showed that the proposed de-nosing method improves its efficiency of feature extraction significantly