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

Yin, X.-X., Hadjiloucas, S., Zhang, Y., Su, M.-Y., Miao, Y. and Abbott, D. (2016) Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs. Artificial Intelligence in Medicine, 67. pp. 1-23. ISSN 09333657

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To link to this item DOI: 10.1016/j.artmed.2016.01.005

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:65905
Publisher:Elsevier

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