Shaping for PET image analysis

Component-trees constitute an efficient data structure for hierarchical image modeling. In particular they are relevant for processing and analyzing images where the structures of interest correspond either to local maxima or local minima of intensity. This is indeed the case of functional data in medical imaging. This motivates the use of component-tree-based approaches for analyzing Positron Emission Tomography (PET) images in the context of oncology. In this article, we present a simple, yet efficient, methodological framework for PET image analysis based on component-trees. More precisely, we show that the second-order paradigm of shaping, that broadly consists of computing the component-tree of a component-tree, provides a relevant way of generalizing the threshold-based strategies classically used by medical practitioners for handling PET images. In addition, it also allows to embed relevant priors regarding the sought cancer lesions. (c) 2020 Elsevier B.V. All rights reserved.