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A Segmentation-Based Method for Metal Artifact Reduction
- Source :
- Academic Radiology. 14:495-504
- Publication Year :
- 2007
- Publisher :
- Elsevier BV, 2007.
-
Abstract
- Rationale and Objectives We propose a novel segmentation-based interpolation method to reduce the metal artifacts caused by surgical aneurysm clips. Materials and Methods Our method consists of five steps: coarse image reconstruction, metallic object segmentation, forward-projection, projection interpolation, and final image reconstruction. The major innovations are 2-fold. First, a state-of-the-art mean-shift technique in the computer vision field is used to improve the accuracy of the metallic object segmentation. Second, a feedback strategy is developed in the interpolation step to adjust the interpolated value based on the prior knowledge that the interpolated values should not be larger than the original ones. Physical phantom and real patient datasets are studied to evaluate the efficacy of our method. Results Compared to the state-of-the-art segmentation-based method designed previously, our method reduces the metal artifacts by 20–40% in terms of the standard deviation and provides more information for the assessment of soft tissues and osseous structures surrounding the surgical clips. Conclusion Mean shift technique and feedback strategy can help to improve the image quality in terms of reducing metal artifacts.
- Subjects :
- Phantoms, Imaging
business.industry
Computer science
Image quality
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation
Iterative reconstruction
Image segmentation
Article
Radiographic Image Enhancement
Metal Artifact
Metals
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Computer vision
Artificial intelligence
Mean-shift
Artifacts
Tomography, X-Ray Computed
business
Algorithms
ComputingMethodologies_COMPUTERGRAPHICS
Interpolation
Subjects
Details
- ISSN :
- 10766332
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- Academic Radiology
- Accession number :
- edsair.doi.dedup.....a7e33b23c02ab2de39afcbf52f85056a