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A comparative study of automatic thresholding approaches for 3D x-ray microtomography of trabecular bone

机译：小梁骨3D X射线显微断层扫描自动阈值化方法的比较研究

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Purpose: This paper presents a comparative study of automatic thresholding algorithms for segmenting trabecular bone volume in x-ray microtomography (μCT). Methods: First, a preprocessing stage was established, which considered noise reduction by applying anisotropic diffusion filtering and contrast enhancement by using morphological top-hats. Next, four automatic thresholding algorithms were implemented: clustering, maximum entropy, moment preservation, and concavity-based. These approaches analyze the preprocessed 3D μCT image histogram to optimize some parameters to find the best gray-level threshold. Thirty-eight vertebra bone samples were acquired from 19 normal Wistar rats, specifically the L3 and L4 vertebrae. The μCT images were acquired with a microfocus x-ray device at 100 slices/sample. Next, three human operators segmented the entire 3D μCT images manually to establish ground-truth segmentations so as to associate the segmentation problem with perceptual grouping. The normalized probabilistic Rand index (NPRI) was used to quantify the agreement between each computerized segmentation and the corresponding set of three ground-truth segmentations. Hence, the NPRI value should tend toward unity for an acceptable performance. Finally, a statistical analysis was done to determine which thresholding approach achieved the best performance. Besides, 3D morphometric indices were also measured. Results: The Games-Howell test (α = 0.05) was used to compare the equality of means from the NPRI results considering the four thresholding algorithms (multiple comparisons). This statistical analysis indicated that the clustering and moment preservation techniques performed similarly, with NPRI values of 0.594 ± 0.126 and 0.607 ± 0.127, respectively. Conclusions: The main advantage of computerized segmentation is that it is fully automatic; that is, no interaction with the user is required. Thus, the method could be considered objective. Besides, the proposed preprocessing stage plays an important role in enhancing the μCT image quality to achieve better separation between the background volume and the trabecular bone volume.
机译：目的：本文对X射线显微断层摄影术（μCT）中用于分割小梁骨体积的自动阈值算法进行比较研究。方法：首先，建立一个预处理阶段，该阶段考虑通过应用各向异性扩散滤波来降低噪声并使用形态学礼帽来增强对比度。接下来，实现了四种自动阈值算法：聚类，最大熵，矩保持和基于凹度。这些方法分析了预处理的3DμCT图像直方图，以优化一些参数以找到最佳灰度阈值。从19只正常Wistar大鼠（特别是L3和L4椎骨）中采集了38个椎骨样品。 μCT图像是用微焦点X射线设备以100切片/样品的速度采集的。接下来，三个操作员手动分割整个3DμCT图像，以建立真实的分割，从而将分割问题与感知分组相关联。使用归一化的概率兰德指数（NPRI）来量化每个计算机化细分与相应的三个真实部分的集合之间的一致性。因此，NPRI值应趋向于统一以获得可接受的性能。最后，进行了统计分析，以确定哪种阈值方法达到了最佳性能。此外，还测量了3D形态计量指标。结果：考虑四个阈值算法（多次比较），使用Games-Howell检验（α= 0.05）比较NPRI结果的均值相等性。该统计分析表明，聚类和矩量保留技术的性能相似，NPRI值分别为0.594±0.126和0.607±0.127。结论：计算机分割的主要优点是它是全自动的。也就是说，不需要与用户进行交互。因此，该方法可以被认为是客观的。此外，所提出的预处理阶段在增强μCT图像质量，实现背景体积与小梁骨体积之间的更好分离方面起着重要作用。

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