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 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.
CITATION STYLE
Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Mendoza, C., Acha, B., … Gómez-Cía, T. (2009). Advanced Concepts for Intelligent Vision Systems. (J. Blanc-Talon, W. Philips, D. Popescu, & P. Scheunders, Eds.) (Vol. 5807, pp. 652-663–663). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-04697-1
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