Name
Abstract | ||
|---|---|---|
Segmentation of pulmonary X-ray computed tomography image is the first step to detect nodule of lung. This paper presents a fully automatic method for segmenting lung from three-dimensional thorax. First, the large airway is removed from lung region by anisotropic diffusion to smooth edges and region-growth. Second, we use an optimal threshold to automatically choose a threshold value, get binary images, and remove vessel of pulmonary using the optimal threshold. Third, left and right lungs are separated by detecting the anterior and posterior junctions using the largest threshold. Finally, we smooth the lung boundary along the mediastinum and lung wall by morphological smoothing. We show that our results are better than that achieved manually. Since there are no accepted criteria for defining the lung boundary near the mediastinum, we believe that our method of defining the boundary of lung near the mediastinum based on the structure of the airway tree provides a good basis for three-dimensional smoothing. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICNC.2007.157 | ICNC |
Keywords | Field | DocType |
threshold value,optimal threshold,accurate lung segmentation,lung wall,lung region,lung boundary,large airway,airway tree,automatic method,largest threshold,x-ray ct images,right lung,anisotropic diffusion,binary image,diffusion,three dimensional,image segmentation | Anisotropic diffusion,Computer vision,X-ray,Lung,Computer science,Segmentation,Binary image,Image segmentation,Smoothing,Artificial intelligence,Mediastinum | Conference |
Volume | ISSN | ISBN |
2 | 2157-9555 | 0-7695-2875-9 |
Citations | PageRank | References |
6 | 0.45 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qixin Gao | 1 | 63 | 2.76 |
| ShengJun Wang | 2 | 16 | 2.06 |
| Dazhe Zhao | 3 | 174 | 25.39 |
| Jiren Liu | 4 | 32 | 5.13 |