Hierarchies and shape-space for PET image segmentation

Abstract : Positron Emission Tomography (PET) image segmentation is essential for detecting lesions and quantifying their metabolic activity. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. In this article, we show how the hierarchical approaches proposed in mathematical morphology can efficiently handle these different strategies. Our contribution is twofold. First, we present the component-tree as a relevant data-structure for developing interactive , real-time, intensity-based segmentation of PET images. Second, we prove that thanks to the recent concept of shaping, we can efficiently involve a priori knowledge for lesion segmentation, while preserving the good properties of component-tree segmenta-tion. Preliminary experiments on synthetic and real PET images of lymphoma demonstrate the relevance of our approach.
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Communication dans un congrès
International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2015), Apr 2015, New York, United States. IEEE, pp.1118-1121, 2015
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Eloïse Grossiord, Hugues Talbot, Nicolas Passat, Michel Meignan, Pierre Tervé, et al.. Hierarchies and shape-space for PET image segmentation. International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2015), Apr 2015, New York, United States. IEEE, pp.1118-1121, 2015. 〈hal-01169944〉

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