Name
Title | ||
|---|---|---|
Region-based graph cut using hierarchical structure with application to ground-glass opacity pulmonary nodules segmentation |
Abstract | ||
|---|---|---|
Image segmentation for the demarcation of pulmonary nodules in CT images is intrinsically an arduous task. The difficulty can be summarized into two aspects. Firstly, lung tumor can be various in terms of physical densities in pulmonary regions, implying the different interpretation as a mixture of GGO and solid nodules. Hence, processing of lung CT images may generally encounter tissue inhomogeneous problem. The second factor that complicates the task of nodule demarcation is the irregular shapes that most nodules are directly connected to other structures sharing the similar density profile. In this paper, an image segmentation framework is proposed by unifying the techniques of statistical region merging and conditional random field (CRF) with graph cut optimization to address the difficult problem of GGO nodules quantification in CT images. Different from traditional segmentation methods that use pixel-based approach such as region growing and morphological constraints, we employ a hierarchical segmentation tree to alleviate the effect of inhomogeneous attenuation. In addition to building perceptual prominent regions, we perform inference in CRF model based on restricting the pool of segmented regions. Following that, an inference CRF model is carried out to detect and localize individual object instances in CT images. The proposed algorithm is evaluated with four sets of manual delineations on 77 lung CT images. Incorporating with the efficiency and accuracy of pulmonary nodules segmentation method proposed in this paper, a computer aided system is hence feasible to develop related clinical application. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2006562 | Proceedings of SPIE |
Keywords | Field | DocType |
Lung CT images,Ground-Glass Opacity nodule segmentation,Statistical region merging,Conditional random field,Graph cut | Cut,Conditional random field,Computer vision,Scale-space segmentation,Segmentation,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Pixel,Physics | Conference |
Volume | ISSN | Citations |
8669 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chi-Hsuan Tsou | 1 | 0 | 0.68 |
| kuolung lor | 2 | 0 | 0.34 |
| Yeun-Chung Chang | 3 | 36 | 5.49 |
| Chung-Ming Chen | 4 | 176 | 16.17 |