Automatic detection of small lung nodules in volumetric CT images
- *Abstract
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Lung cancer ranks among the leading causes of cancer death in the U.S. Although the scientific jury is still out regarding the impact of earlier lung cancer detection on patient mortality, improvements in diagnostic imaging technology continue to make earlier detection of lung cancer possible - and intuitively desirable.
Investigators at the Weill Cornell Medical College Department of Radiology have been pioneers in the field of early lung cancer detection. A member of that department, Binsheng Zhao, has devised an algorithm that automatically detects nodules.
The algorithm identifies local maximal densities on volumetric chest CT images by successively decreasing threshold values and testing 3D geometric overlaps of the objects (an object being defined as a group of connected voxels whose density values are greater than the threshold value) detected at the current and previous threshold levels.
Potential Applications
Medical imaging diagnostic for lung cancer. Product can be integrated with software packages provided with CT scanners, or can be provided as part of a desktop-based product for analysis of CT images.
- *Licensing
- Vibhu Sachdev(212) 746-6187sachdev@cornell.edu
- 其他
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- Zhao B et al. 2003 Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Med Phys. 4(3):248-60
- Zhao B, et al (2005) Pulmonary metastases: effect of CT section thickness on measurement--initial experience. Radiology 234(3):934-9
- 国家/地区
- 美国
