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

Publication date: Available online 16 February 2016
Source:Artificial Intelligence in Medicine
Author(s): Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang, Min-Ying Su, Yuan Miao, Derek Abbott
ObjectiveWe 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.MethodsBoth 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 also discussed. These include Mahalanobis distance, support vector machine (SVM) and extreme learning machine (ELM) classifiers.ValidationExamples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.ResultsIdentifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.ConclusionThe 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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