Artículo
Self-assessed Contrast-Maximizing Adaptive Region Growing
Autor/es | Sánchez Mendoza, Carlos
Acha Piñero, Begoña Serrano Gotarredona, María del Carmen Gómez-Cía, Tomás |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2009 |
Fecha de depósito | 2021-12-20 |
Publicado en |
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Resumen | 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 ... 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. |
Cita | Sánchez 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. |
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