Histogram Feature–Based Classification Improves Differentiability of Early Bone Healing Stages From Micro-Computed Tomographic Data
Histogram Feature–Based Classification Improves Differentiability of Early Bone Healing Stages From Micro-Computed Tomographic Data
复制标题
基于直方图特征的分类提高了微计算机断层扫描数据中早期骨愈合阶段的可区分性
DOI：
10.1097/rct.0b013e31825eae8a
复制
发表时间：
2012
影响因子：
1.3
通讯作者：
RaumK.
中科院分区：
文献类型：
--
作者：
PreiningerB;HesseB;RohrbachD;VargaP;GerigkH;LangerM;PeyrinF;PerkaC;RaumK.
未完全矿化的组织之间的对比度较弱，限制了传统的计算机断层扫描（CT）。一个自动化的灰度直方图为基础的分析功能，可以提高早期骨healing.Materials和MethodsTissue形成在大鼠截骨模型的敏感性进行了分析，使用在体内micro-CT和组织学分类（矿化，软骨，结缔组织）。一个传统的阈值为基础的方法，包括手动轮廓进行了比较，一个新的时刻为基础的方法：去除背景峰后，每个切片的直方图的特点是由他们的时刻和分析作为一个功能的位置沿着长bone axies.ResultsThe阈值为基础的方法可以区分矿化和结缔组织（R2 = 0.73）。基于矩的方法在所有3组之间产生了明显的区分，分类准确度高达R2 = 0. 93。结论基于矩的评估在对愈合阶段的敏感性、用户独立性和时间消耗方面优于传统的基于阈值的CT分析。
ObjectiveContrast between not fully mineralized tissues is weak and limits conventional computed tomography (CT). An automated grayscale histogram-based analysis features could improve the sensitivity to tissue alterations during early bone healing.Materials and MethodsTissue formation in a rat osteotomy model was analyzed using in vivo micro-CT and classified histologically (mineralized, cartilage, and connective tissues). A conventional threshold-based method including manual contouring was compared to a novel moment-based method: after removing the background peak, the histograms of each slice were characterized by their moments and analyzed as a function of the position along the long bone axis.ResultsThe threshold-based method could differentiate between the mineralized and connective tissue (R 2= 0.73). The moment-based approach yielded a clear distinction between all 3 groups with a classification accuracy up to R 2= 0.93.ConclusionsThe moment-based evaluation outperforms the conventional threshold-based CT analysis in sensitivity to the healing stage, user independence, and time consumption.
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DOI：
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