Application of fuzzy skeletonization ot quantitatively assess trabecular bone micro-architecture.
Application of fuzzy skeletonization ot quantitatively assess trabecular bone micro-architecture.
复制标题
应用模糊骨架化定量评估骨小梁微结构。
DOI：
10.1109/embc.2013.6610342
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Saha,PunamK
中科院分区：
文献类型：
--
作者：
Jin,Dakai;Liu,Yinxiao;Saha,PunamK
成人骨疾病，特别是骨质疏松症，导致骨折的风险增加，与大量的发病率，死亡率和经济成本。在临床上，骨质疏松症的定义是低骨矿物质密度;然而，越来越多的证据表明，骨小梁（TB）的微结构质量是骨强度和骨折风险的重要决定因素。骨架化在提供TB网络的紧凑表示中发挥着重要作用，可以计算与TB微结构相关的多个定量参数。对于二进制数字对象，三维数字化的文献已经相当成熟。然而，模糊对象的去噪的挑战大多是没有答案的。在这里，提出了一种算法，用于模糊聚类，使用模糊草火传播和分支级噪声修剪策略，最后，它的应用TB微架构评估的影响。具体而言，模糊卷积算法应用于计算TB板，板/棒比，厚度和间距。最后，对12具尸体的骨强度进行了实验研究，结果令人鼓舞，线性相关系数R2分别高达0.93、0.88、0.85和0.86。
Adult bone diseases, especially osteoporosis, lead to increased risk of fracture associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density; however, increasing evidence suggests that the micro-architectural quality of trabecular bone (TB) is an important determinant of bone strength and fracture risk. Skeletonization plays an important role providing a compact representation of TB network that allows computation of several quantitative parameters relating to TB micro-architecture. Literature of three-dimensional skeletonization is quite matured for binary digital objects. However, the challenges of skeletonization for fuzzy objects are mostly unanswered. Here, an algorithm for fuzzy skeletonization is presented using fuzzy grassfire propagation and a branch-level noise pruning strategy and, finally, its application to TB micro-architectural assessment is investigated. Specifically, the fuzzy skeletonization algorithm is applied to compute TB plateness, plate/rod ratio, thickness, and spacing. Finally, the effectiveness of these measures to predict experimental bone strength is investigated on twelve cadaveric specimens and the results are encouraging with the R2value of linear correlation with bone strength being as high as 0.93, 0.88, 0.85 and 0.86, respectively.