title: 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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia. Electronic address: xiaoxia.yin@vu.edu.au.
authors.forename: Xiao-Xia
authors.initials: XX
authors.lastname: Yin
authors.affiliation: School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK.
authors.forename: Sillas
authors.initials: S
authors.lastname: Hadjiloucas
authors.affiliation: Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA.
authors.forename: Min-Ying
authors.initials: MY
authors.lastname: Su
authors.affiliation: College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Miao
authors.affiliation: Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, South Australia, SA 5000, Australia.
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
mesh_terms: D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D060388:Support Vector Machine
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