Automatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Mining Lung Shape from X-Ray Images
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Lung tumor segmentation in PET images using graph cuts
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
This paper proposes an approach to the segmentation of lung fields in the severe acute respiratory syndrome (SARS) infected radiographic images, which is the first step towards a computer-aided diagnosis system. To overcome the segmentation difficulty of highly atypical property of SARS in the lung images, our algorithm first uses morphological operations to obtain the initial estimation of the regions where the lung boundaries lie in, and then applies a new graphbased optimization method to find the interested regions. The theoretical analysis shows that our approach is resistant to boundary discontinuity, noise, and large patches that affect the boundary search. Experimental results are given to demonstrate the good performance of our algorithm.