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.

Keywords: Basal cell carcinomas; Complex extreme learning machine; Computer-aided diagnosis; Dynamic contrast-enhanced magnetic resonance images; Extreme learning machine; Mahalanobis distance; Multi-channel signal processing; Poly(dA-dT)-poly(dT-dA) DNA; Principal component analysis; Quaternary classification; Support vector machine; Tensor algebra; Terahertz pulse imaging; Time domain spectroscopy; Tumour microvasculature.