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Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest
We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are i...
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| 出版年: | Proc SPIE Int Soc Opt Eng |
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| 主要な著者: | , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6711599/ https://ncbi.nlm.nih.gov/pubmed/31456600 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/12.2292886 |
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