Gray-level hit-or-miss transform based region-growing for automatic segmentation of 3D coronary arteries

Bessem Bouraoui 1, 2 Christian Ronse 1 Joseph Baruthio 2 Nicolas Passat 1 Philippe Germain 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 Service de Radiologie (Hôpitaux Universitaires de Strasbourg)
Abstract : In this paper we propose a fully automatic algorithm for coronary artery extraction from X-ray data (3D- CT scan, 64 detectors). Growing and thresholding methods, in their most general form, are not sufficient to extract only the whole coronary arteries, because of the properties of these images. Finding appropriate methods is known to be a challenging problem because of the data imperfections such as noise, heterogeneous intensity and contrasts of similar tissues. We deal with these challenges by employing discrete geometric tools to fit on the arteries form independently from any perturbation of the data.
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Bessem Bouraoui, Christian Ronse, Joseph Baruthio, Nicolas Passat, Philippe Germain. Gray-level hit-or-miss transform based region-growing for automatic segmentation of 3D coronary arteries. International Symposium on Mathematical Morphology (ISMM), 2007, Rio de Janeiro, Brazil. pp.23-24. ⟨hal-01695000⟩

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