Xiao-Xia Yin1, Sillas Hadjiloucas2, Yanchun Zhang3, Min-Ying Su4, Yuan Miao5, Derek Abbott6. 1. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia. Electronic address: xiaoxia.yin@vu.edu.au. 2. School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK. 3. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia. 4. Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA. 5. College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia. 6. Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, South Australia, SA 5000, Australia.
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.
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.