An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
MAP-MRF segmentation of lung tumours in PET/CT images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Fuzzy region competition: a convex two-phase segmentation framework
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Image Processing
Discriminative pathological context detection in thoracic images based on multi-level inference
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. In this paper, we propose the use of a variational segmentation method, based on the minimization of the TV norm and a convex formulation, for segmenting thoracic pairs of PET and CT images, in the context of radiotherapy planning. We first highlight the limitations of a pure vectorial formulation of the variational segmentation method for PET and CT images. We then propose to better exploit the bi-modality by introducing a parameter which varies spatially depending on the PET intensity to adjust precisely the segmentation of CT images. Segmentation results on lung tumors and lymphatic nodes are shown, and comparisons performed with manual segmentations illustrate the quality of the results.