Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Zheng, Yuanjie ^a   Steiner, Karl ^b   Bauer, Thomas ^c   Yu, Jingyi ^a   Shen, Dinggang ^d   Kambhamettu, Chandra ^a  

^a University of Delaware   (United States)

^b UNIVERSITY OF DELAWARE   (United States)

^c HELEN F GRAHAM CANCER CENTER   (United States)

^d UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOLOGICAL ORGANS; COMPUTER NETWORKS; COMPUTER VISION; DEFORMATION; ELECTRIC INSTRUMENT TRANSFORMERS; IMAGE PROCESSING; SPLINES; THREE DIMENSIONAL;

CONTROL MESHES; CT DATA;

TUMORS;

EID: 50649105291     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4409150     Document Type: Conference Paper

References (16)

1. Y. S. Akgul and C. Kambhamettu. A coarse-to-fine deformable contour optimization framework. IEEE Trans. Pattern Anal. Mach. Intell., 25(2):174-186, 2003.
2. K. Awai and et al. Pulmonary nodules at chest CT: Effect of computer-aided diagnosis on radiologists detection performance. Radiology, 230:347-352, 2004.
3. C. A. Good and T. W. Wilson. The solitary circumscribed pulmonary nodule: study of 705 cases encountered roentgenologically in a period of three and one-half years. J. Am. Med. Assoc., 166:210-215, 1958.
4. S. G. Jenning and et al. Lung tumor growth: assessment with CT-comparison of diameter and cross-sectional area with volume measurements. Radiology, 231(3):866-871, 2004.
5. K. G. Kim and et al. Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: Initial experience. Radiology, 237:657-661, 2005.
6. W. J. Kostis and et al. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images. IEEE Trans. Med. Img., 22(10):1259-1273, 2003.
7. D. Loeckx and et al. Nonrigid image registration using free-form deformations with a locally rigidity constraint, 2004. MICCAI.
8. A. Moreno and et al. Non-linear registration between 3d images including rigid objects: Application to CT and PET lung images with tumors, 2006. In Workshop on Image Registration in Deformable Environments, DEFORM 2006.
9. A. P. Reeves and et al. On measuring the change in size of pulmonary nodules. IEEE Trans. Med. Img., 25(4):435-450, 2006.
10. A. P. Reeves and W. J. Kostis. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am., 38(3):497-509, 2000.
11. T. Rohlfing and J. C. R. Maurer. Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Infor. Tech. In Biomedicine, 7(1):16-25, 2003.
12. D. Rueckert and et al. Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imag., 18:712-721, 1999.
13. C. Tanner and et al. Volume and shape preservation of enhancing lesions when applying non-rigid registration to a time series of contrast enhancing MR breast images, 2000. MICCAI.
14. A. Yezzi and et al. A variational framework for joint segmentation and registration, 2001. MMBIA.
15. R. Zabih and V. Kolmogorov. Spatially coherent clustering using graph cuts, 2004. CVPR'04.
16. Y. Zheng and et al. De-enhancing the dynamic contrast-enhanced breast mri for robust registration, 2007. MICCAI.