Sánchez Mendoza, CarlosAcha Piñero, BegoñaSerrano Gotarredona, María del CarmenGómez-Cía, Tomás2021-12-202021-12-202009Sánchez Mendoza, C., Acha Piñero, B., Serrano Gotarredona, M.d.C. y Gómez-Cía, T. (2009). Self-assessed Contrast-Maximizing Adaptive Region Growing. Lecture Notes in Computer Science, 652-663.0302-9743https://hdl.handle.net/11441/128505In 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 self-assessed 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.application/pdf12 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/CTSegmentationRegion-growingSeedMuscleBoneFaitSurgical planningVirtual realityinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess