Technology Information Laboratory, Center for Research and Advanced Studies of the National Polytechnic Institute, Ciudad Victoria, 87130 Tamaulipas, Mexico.
Med Phys. 2013 Sep;40(9):091903. doi: 10.1118/1.4817235.
This paper presents a comparative study of automatic thresholding algorithms for segmenting trabecular bone volume in x-ray microtomography (μCT).
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.
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.
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.
本研究旨在比较用于 X 射线微计算机断层扫描（μCT）中骨小梁体积分割的自动阈值算法。
首先，建立了一个预处理阶段，通过应用各向异性扩散滤波来降低噪声，并使用形态学顶帽进行对比度增强。然后，实现了四种自动阈值算法：聚类、最大熵、矩保持和基于凹度。这些方法分析预处理的 3D μCT 图像直方图，以优化一些参数，找到最佳灰度阈值。从 19 只正常 Wistar 大鼠中获得 38 个椎骨样本，具体为 L3 和 L4 椎骨。使用微焦点 X 射线设备以 100 个切片/样本的速度采集 μCT 图像。然后，由三名操作人员手动分割整个 3D μCT 图像，以建立真实分割，从而将分割问题与感知分组联系起来。归一化概率 Rand 指数（NPRI）用于量化每个计算机化分割与相应的三组真实分割之间的一致性。因此，对于可接受的性能，NPRI 值应趋于 1。最后，进行了统计分析，以确定哪种阈值方法具有最佳性能。此外，还测量了 3D 形态计量指标。
采用 Games-Howell 检验（α=0.05）比较四种阈值算法（多重比较）的 NPRI 结果的均值相等性。这项统计分析表明，聚类和矩保持技术表现相似，NPRI 值分别为 0.594±0.126 和 0.607±0.127。
计算机化分割的主要优点是它是全自动的，即无需与用户交互。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 图像质量方面发挥了重要作用，以实现背景体积和骨小梁体积之间更好的分离。
