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Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

, , , and . Medical Image Analysis, 18 (1): 176-196 (2014)

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When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum., , and . CoRR, (2017)Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames, , , , , and . IEEE Transactions on Information Technology in Biomedicine, (2012)Brain decoding: Opportunities and challenges for pattern recognition., and . Pattern Recognit., 45 (6): 2033-2034 (2012)From Nano to Macro: An overview of the IEEE Bio Image and Signal Processing Technical Committee., , , , , , , , and . IEEE Signal Process. Mag., 40 (4): 61-71 (June 2023)Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience., , , and . IEEE Signal Process. Mag., 30 (3): 58-70 (2013)Analytical Footprints: Compact Representation of Elementary Singularities in Wavelet Bases., , , and . IEEE Trans. Signal Process., 58 (12): 6105-6118 (2010)Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training., , , , and . IEEE Trans. Biomed. Eng., 64 (6): 1228-1237 (2017)Robust non-linear filtering for video processing., , and . DSP, page 571-574. IEEE, (2002)WSPM or How to Obtain Statistical Parametric Maps using Shift-Invariant Wavelet Processing., , and . ICASSP (5), page 1101-1104. IEEE, (2006)Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , and 38 other author(s). NeuroImage, (August 2021)