ArticlePeerJ2025
Bone-tissue decomposition of a single X-ray image
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Because bones are often enveloped by soft tissues, their visibility in X-ray images is compromised, resulting in a lack of clarity. Addressing this challenge, our article introduces an innovative approach to virtually decompose an X-ray image into distinct components: one representing soft tissues and the other, the bone structure. To achieve this separation, we have formulated a novel mathematical model. With proper assumptions, the model is reduced to a standard Laplace equation, which has fast numerical solvers. Our method has two important properties. First, the bone image derived from this process is theoretically guaranteed to have enhanced contrast relative to the original, thereby accentuating the visibility of bony details. Second, our method is computationally fast. Our method can process a
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.