Wavelet denoise on MRS data based on ICA and PCA

  • Authors:

  • Jian Ma;Zengqi Sun;Guangbo Dong;Guihai Xie

  • Affiliations:

  • Department of Computer Science, Tsinghua Univ., Beijing, China;Department of Computer Science, Tsinghua Univ., Beijing, China;Department of Computer Science, Tsinghua Univ., Beijing, China;Department of Control System Engineering, Ordnance Engineering College, Shijiazhuang, Hebei, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

In this paper, experiments on previous works of automatic decomposition of MRS based on PCA and ICA were conducted on our small amount of low SNR dataset. New experimental results were derived. Results show that only PCA cannot decomposes MRS into meaningful components when small amount of low SNR data are available and that the denoise ability of PCA is limited and heavily affected result of consequent ICA. A new method combined wavelet with PCA is proposed. Experimental results on the dataset show the improvement presented by wavelet. The new method is promising to further research, such as MRS interpretation and classification.