Feature Analysis and De-noising of MRS Data Based on Pattern Recognition and Wavelet Transform
Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums（2006）
摘要
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
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关键词
wavelet basis function,innovative feature extraction method,pattern recognition,spectroscopy mrs data,wavelet transforms,magnetic resonance spectroscopy,mrs data,de-noising method,data analysis,feature analysis,image denoising,svm,image recognition,feature extraction method,independent component analysis,new wavelet basis function,conventional wavelet basis function,cancer,feature extraction,support vector machine,biomedical mri,free induction decay data,mrs data denoising,wavelet filter,new wavelet filter set,proposed denosing method,wavelet transform,mrs data feature analysis,spectroscopy mrs data analysis,mrs spectrum,support vector machines,medical image processing,wavelet threshold,effective method,spectroscopy,nuclear magnetic resonance,wavelet analysis,free induction decay,spectrum,pattern analysis,noise reduction
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