Attribute-filtering and knowledge extraction for vessel segmentation

Benoît Caldairou; Nicolas Passat; Benoît Naegel

doi:10.1007/978-3-642-17289-2_2

Conference papers

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Image Processing [eess.IV]

Computer Science [cs] / Medical Imaging

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https://hal.univ-reims.fr/hal-01695035
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Submitted on : Saturday, March 3, 2018 - 5:10:21 PM
Last modification on : Thursday, October 14, 2021 - 11:06:02 AM
Long-term archiving on: : Monday, June 4, 2018 - 3:53:27 PM

Attribute-filtering and knowledge extraction for vessel segmentation

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Attribute-filtering and knowledge extraction for vessel segmentation

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