Sciweavers

Share
ACIVS
2009
Springer

Self-assessed Contrast-Maximizing Adaptive Region Growing

12 years 4 months ago
Self-assessed Contrast-Maximizing Adaptive Region Growing
In the context of an experimental virtual-reality surgical planning software platform, we propose a fully self-assessed adaptive region growing segmentation algorithm. Our method successfully delineates main tissues relevant to head and neck reconstructive surgery, such as skin, fat, muscle/organs, and bone. We rely on a standardized and selfassessed region-based approach to deal with a great variety of imaging conditions with minimal user intervention, as only a single-seed selection stage is required. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing regions. Validation based on synthetic images, as well as truly-delineated real CT volumes, is provided for the reader’s evaluation. Key words: CT, segmentation, region-growing, seed, muscle, bone, fat, surgical planning, virtual reality
Carlos S. Mendoza, Begoña Acha, Carmen Serr
Added 23 Jul 2010
Updated 23 Jul 2010
Type Conference
Year 2009
Where ACIVS
Authors Carlos S. Mendoza, Begoña Acha, Carmen Serrano, Tomás Gómez-Cía
Comments (0)
books