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

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Yin XX,Hadjiloucas S,Zhang Y,Su MY,Miao Y,Abbott D

doi

10.1016/j.artmed.2016.01.005

subject

Has Abstract

pub_date

2016-02-01 00:00:00

pages

1-23

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(16)30011-2

journal_volume

67

pub_type

杂志文章

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