Feature-based supervised lung nodule segmentation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Campos, DM (Autor) Outra; Simoes, A (Autor) Outra; Ramos, I (Autor) Outra; Aurélio Campilho (Autor) FEUP

Ata de Conferência Internacional

Título: IFMBE Proceedings

Páginas: 23-26

International Conference on Health Informatics, ICHI 2013

7 November 2013 through 9 November 2013

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00A-BG3

DOI: 10.1007/978-3-319-03005-0_7

Abstract (EN): Lung nodule segmentation allows for automatic measurement of the nodule's size or volume which is of utmost importance in lung cancer diagnosis. It is a challenging task since there are many different types of nodules (solid or non-solid, solitary or multiple, etc). A supervised lung nodule segmentation method uses a shape-based, contrast-based and intensity-based feature set to produce three preliminary segmentations and an artificial neural network to obtain a more accurate segmentation. This method was applied to 20 computer tomography studies, all containing nodules. The data has 10 images of solid nodules and 10 images of ground glass opacity nodules, all with ground-truth. The segmentation uses a region growing approach and the volumetric shape index is used for nodule detection and providing a seed point. In the first and second segmentation the probability of each neighbor belonging to the nodule is estimated using the volumetric shape index and the convergence index filter, respectively. The third segmentation is obtained using a feature set region regression method where for each neighbor the probability of belonging to the nodule or not is obtained using k nearest neighbor regression. Then, using a leave-one out method, an artificial neural network uses the three preliminary segmentations as input and is trained to obtain a more accurate segmentation. Results obtained a 12% relative volume error, 88% and 93% Jaccard and Dice coefficient respectively. © 2014, Springer International Publishing Switzerland.

Idioma: Inglês

Tipo (Avaliação Docente): Científica