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Computerised analysis of osteoporotic bone patterns using texture parameters characterising bone architecture

机译:使用表征骨结构的纹理参数对骨质疏松性骨图案进行计算机分析

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Objective: To 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. Methods: 32 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). Results: The 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. Conclusion: Measurements 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.
机译:目的:利用均方根(RMS)值,第一矩功率谱(FMP)值和分形维数来评估骨质疏松性骨小梁图案的几何变化。通过使用这些方法,我们尝试使用表征骨结构的纹理参数和骨质疏松的计算机辅助诊断来对骨质疏松骨图案进行计算机分析。方法:对高丽大学九老医院收治的32例患者进行分析。患者年龄为51至89岁,平均年龄为65岁。通过确定曲线下面积(AUC)来执行接收器工作特性曲线分析。结果:骨矿物质密度(BMD)测量值(AUC = 0.78)比RMS,FMP和分形维数值(AUC = 0.72)是更好的骨量指标;因此,RMS,FMP和分形维数的组合可以更好地指示骨骼质量。结论:结合使用BMD测量值和RMS值以及结合FMP和分形维数值(AUC = 0.85)的测量产生的结果要好于分别使用两个参数集来诊断骨质疏松症。知识的进步:为了更有效地应用,将需要对更多案例和数据进行进一步研究。

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