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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Mendoza, C. S., Acha, B., Serrano, C., & Gómez-Cía, T. (2009). Self-assessed contrast-maximizing adaptive region growing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5807 LNCS, pp. 652–663). Springer Verlag. https://doi.org/10.1007/978-3-642-04697-1_61
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