Surface Simplex Meshes for 3D Medical Image Segmentation

Johan Montagnat (1) , Hervé Delingette (1) , Nicolas Scapel (1) , Nicholas Ayache (1)

1 EPIDAURE - Medical imaging and robotics

Medical image segmentation is often a difficult task due to the low contrast, the low signal/noise ratio and the presence of outliers in images. However, it remains a critical issue for image interpretation, pattern recognition and automatic diagnosis. Deformable models are well-suited for capturing the geometry and the shape variability of anatomical structures from medical images. Indeed, they introduce an a priori knowledge in the segmentation process that increases its robustness to noise and outliers. In this paper, we address many problems related to volumetric medical image segmentation based on deformable models including model initialization, model topology, deformation behavior and image features extraction.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Johan Montagnat, Hervé Delingette, Nicolas Scapel, Nicholas Ayache. Surface Simplex Meshes for 3D Medical Image Segmentation. International Conference on Robotics and Automation (ICRA00), Apr 2000, San Francisco, CA, United States. pp.864 - 870, ⟨10.1109/ROBOT.2000.844158⟩. ⟨hal-00691695⟩