title: Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.
abstract: We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.
authors.affiliation: Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia. Electronic address: xiaoxia.yin@vu.edu.au.
authors.forename: X-X
authors.initials: XX
authors.lastname: Yin
authors.affiliation: Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia; School of Computer Science, Fudan University, Shanghai, China. Electronic address: yanchun.zhang@vu.edu.au.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Nanjing University of Finance and Economics school of Computer Science, Nanjing, China.
authors.forename: J
authors.initials: J
authors.lastname: Cao
authors.affiliation: Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China. Electronic address: wujianlin@vip.163.com.
authors.forename: J-L
authors.initials: JL
authors.lastname: Wu
authors.affiliation: School of Biological Sciences and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK. Electronic address: s.hadjiloucas@reading.ac.uk.
authors.forename: S
authors.initials: S
authors.lastname: Hadjiloucas
mesh_terms: D006801:Humans; D007858:Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D060388:Support Vector Machine
keywords: Complex extreme learning machine; Deep learning; MRI; THz imaging; Wavelet analysis