ArticleThe British journal of radiology2013
Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture.
Article in The British journal of radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography.BMC medical imaging · 2022Trial
- Radiomics models based on thoracic and upper lumbar spine in chest LDCT to predict low bone mineral density.Scientific reports · 2024Article
- Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images.Bioengineering (Basel, Switzerland) · 2024Article
- Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features.BioMed research international · 2019Article
- Effect of radiation dose reduction on texture measures of trabecular bone microstructure: an in vitro study.Journal of bone and mineral metabolism · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
objectiveTo evaluate the geometric change of osteoporotic bone trabecular patterns using root mean square (RMS) values, first moment power spectrum (FMP) values and fractal dimension values. With the use of these methods, we attempted computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture and computer-aided diagnosis of osteoporosis.
methods32 patient cases from Korea University Guro Hospital were analysed. Patient ages ranged from 51 to 89 years, with a mean age of 65 years. Receiver operating characteristic curve analysis was performed with determination of the area under the curve (AUC).
resultsThe bone mineral density (BMD) measurement (AUC=0.78) was a better indicator of bone quantity than the RMS, FMP and fractal dimension values (AUC=0.72) for diagnosis; therefore the combination of RMS, FMP and fractal dimension values was a better indicator of bone quality.
conclusionMeasurements that combined BMD measurement and RMS values and combined FMP and fractal dimension values (AUC=0.85) together produced better results than the use of the two parameter sets separately for a diagnosis of osteoporosis. ADVANCES IN KNOWLEDGE: For more effective application, additional study on more cases and data will be required.
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