WSPM: Wavelet-based statistical parametric mapping
By D. Van de Ville, Mohamed L. Seghier, François Lazeyras, Thierry Blu, Michael A. Unser. 2007
50 citations recorded by the source corpus.
Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to "improve" the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the spatial bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing
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Citations in the corpus
- Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “Machine learning based methods Davatzikos (2004) Cuingnet et al. (2011) De Martino et al. (2008) Fan et al. (2007) Gaonkar and Davatzikos (2012) Xu et al. (2009) Van De Ville et al. (2007) Van De Ville et al. (2004) Mingoia et al. (2012) can offer diagnostics based on neuroimaging data.”
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “For example Bayesian Markov random field models [173], rough set theory [174], higherorder singular value decomposition [175], wavelets [176], independent component analysis [177], or higher order PDEs [178].”