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Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs.

  • Author(s): Yin, Xiao-Xia
  • Hadjiloucas, Sillas
  • Zhang, Yanchun
  • Su, Min-Ying
  • Miao, Yuan
  • Abbott, Derek
  • et al.
Abstract

OBJECTIVE:We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS:Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. VALIDATION:Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. RESULTS:Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. CONCLUSION:The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.

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