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Structural adaptation is a technique from nonparametric statistics that enables the reduction of noise in data, e.g., from medical imaging applications, without blurring the structural borders that are given by tissue borders. In this talk, I will present the general principles of this approach as well as its application to specific magnetic resonance imaging modalities and demonstrate, how it can be used for improved inference from the acquired data.
This presentation is part of Minisymposium “MS5 - Learning and adaptive approaches in image processing (2 parts)”
organized by: Kostas Papafitsoros (Weierstrass Institute Berlin) , Michael Hintermüller (Humboldt University and Weierstrass Institute Berlin) .