Name
Abstract | ||
|---|---|---|
We present a probability model for lung airways in computed tomography (CT) images. Lung airways are tubular structures that display specific features, such as low intensity and proximity to vessels and bronchial walls. From these features, the posterior probability for the airway feature space was computed using a Bayesian model based on 20 CT images from subjects with different degrees of Chronic Obstructive Pulmonary Disease (COPD). The likelihood probability was modeled using both a Gaussian distribution and a nonparametric kernel density estimation method. After exhaustive feature selection, good specificity and sensitivity were achieved in a cross-validation study for both the Gaussian (0.83, 0.87) and the nonparametric method (0.79, 0.89). The model generalizes well when trained using images from a late stage COPD group. This probability model may facilitate airway extraction and quantitative assessment of lung diseases, which is useful in many clinical and research settings. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISBI.2013.6556491 | ISBI |
Keywords | Field | DocType |
biomedical research,kernel,atmospheric modeling,ct,estimation theory,gaussian distribution,bioinformatics,image segmentation,feature extraction,computed tomography,computational modeling,bayesian model | Feature vector,Bayesian inference,Pattern recognition,Feature selection,Computer science,Posterior probability,Nonparametric statistics,Feature extraction,Artificial intelligence,Estimation theory,Kernel density estimation | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4673-6456-0 | 0 |
PageRank | References | Authors |
0.34 | 4 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rina Dewi Rudyanto | 1 | 31 | 2.89 |
| Arrate Muñoz-Barrutia | 2 | 123 | 20.02 |
| Alejandro Díaz | 3 | 24 | 2.24 |
| James C. Ross | 4 | 78 | 8.89 |
| George R. Washko | 5 | 84 | 11.32 |
| Carlos Ortiz-de-Solorzano | 6 | 233 | 23.07 |
| Raúl San José Estépar | 7 | 238 | 29.27 |