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Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms
Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns...
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| Huvudupphovsmän: | , , , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
Springer-Verlag
2011
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3348983/ https://ncbi.nlm.nih.gov/pubmed/22089834 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10278-011-9435-5 |
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