DocumentCode :
881692
Title :
On measuring the change in size of pulmonary nodules
Author :
Reeves, Anthony P. ; Chan, Antoni B. ; Yankelevitz, David F. ; Henschke, Claudia I. ; Kressler, Bryan ; Kostis, William J.
Author_Institution :
Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
Volume :
25
Issue :
4
fYear :
2006
fDate :
4/1/2006 12:00:00 AM
Firstpage :
435
Lastpage :
450
Abstract :
The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.
Keywords :
cancer; computerised tomography; image matching; image registration; image segmentation; lung; medical image processing; adaptive thresholding; computed tomography image scans; image registration; isotropic resampling; knowledge-based shape matching; lung cancer; malignancy; nodule location; nodule sizing; nodules; pleural segmentation; pulmonary nodule size changes; volume change measurement; Biopsy; Cancer; Computed tomography; Image registration; Image segmentation; Lungs; Radiography; Shape; Size measurement; Volume measurement; Computed tomography; growth rate estimation; image registration; image segmentation; pulmonary nodules; rule-based segmentation; Algorithms; Artificial Intelligence; Coin Lesion, Pulmonary; Humans; Imaging, Three-Dimensional; Information Storage and Retrieval; Pattern Recognition, Automated; Phantoms, Imaging; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Radiography, Thoracic; Reproducibility of Results; Sensitivity and Specificity; Severity of Illness Index; Subtraction Technique; Tomography, X-Ray Computed;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2006.871548
Filename :
1610748
Link To Document :
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