Article
Self-assessed Contrast-Maximizing Adaptive Region Growing
Author/s | Sánchez Mendoza, Carlos
Acha Piñero, Begoña Serrano Gotarredona, María del Carmen Gómez-Cía, Tomás |
Department | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Publication Date | 2009 |
Deposit Date | 2021-12-20 |
Published in |
|
Abstract | 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. |
Citation | 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. |
Files | Size | Format | View | Description |
---|---|---|---|---|
10.1.1.604.4569.pdf | 764.8Kb | [PDF] | View/ | |