3 papers
math.NA2022
Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes Approach
Davide Poggiali, Diego Cecchin, Stefano De Marchi
It is a common practice in multimodal medical imaging to undersample the anatomically-derived segmentation images to measure the mean activity of a co-acquired functional image. Th…
math.NA2022
Variably Scaled Persistence Kernels (VSPKs) for persistent homology applications
Stefano De Marchi, Federico Lot, Francesco Marchetti +1
In recent years, various kernels have been proposed in the context of persistent homology to deal with persistence diagrams in supervised learning approaches. In this paper, we con…
math.NA2021
Oversampling errors in multimodal medical imaging are due to the Gibbs effect
Davide Poggiali, Diego Cecchin, Cristina Campi +1
To analyse multimodal 3-dimensional medical images, interpolation is required for resampling which - unavoidably - introduces an interpolation error. In this work we consider three…