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Attribute-filtering and knowledge extraction for vessel segmentation

Benoît Caldairou 1, 2 Nicolas Passat 1 Benoît Naegel 3
1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
2 LINC - Laboratoire d'Imagerie et de Neurosciences Cognitives
3 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Attribute-filtering, relying on the notion of component-tree, enables to process grey-level images by taking into account high-level a priori knowledge. Based on these notions, a method is proposed for automatic segmentation of vascular structures from phase-contrast magnetic resonance angiography. Experiments performed on 16 images and validations by comparison to results obtained by two human experts emphasise the relevance of the method.
Keywords : magnetic resonance angiography component-trees vessel segmentation mathematical morphology
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Benoît Caldairou, Nicolas Passat, Benoît Naegel. Attribute-filtering and knowledge extraction for vessel segmentation. International Symposium on Visual Computing (ISVC), 2010, Las Vegas, United States. pp.13-22, ⟨10.1007/978-3-642-17289-2_2⟩. ⟨hal-01695035⟩

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