Name
Abstract | ||
|---|---|---|
Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data(1). Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and lambda, the terminal coefficient for t-links We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of lambda influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 similar to 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and lambda parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and lambda=0.5 yielded good results. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2219897 | Proceedings of SPIE |
Keywords | Field | DocType |
Image segmentation,3D Lung CT,Graph-cuts | Cut,Computer vision,Data set,Segmentation,Image segmentation,Artificial intelligence,Computed tomography,3d image,Physics | Conference |
Volume | ISSN | Citations |
9788 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung won Cha | 1 | 1 | 1.02 |
| Neal Dunlap | 2 | 13 | 4.00 |
| Brian Wang | 3 | 33 | 4.60 |
| Amir A. Amini | 4 | 443 | 63.30 |