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

• Type: Conference
• Year: 2009
• Where: ACIVS
• Authors: Carlos S. Mendoza, Begoña Acha, Carmen Serrano, Tomás Gómez-Cía
• Keywords: ACIVS 2009, Region Growing Segmentation, Surgical Planning, Virtual-reality Surgical Planning