Assessment of bone feature parameters from lumbar trabecular skeletal patterns using mathematical morphology image processing
Assessment of bone feature parameters from lumbar trabecular skeletal patterns using mathematical morphology image processing
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DOI：
10.1007/s007740200029
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发表时间：
2002-01-01
影响因子：
3.3
通讯作者：
Kashima, I
中科院分区：
文献类型：
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作者：
Oka, K;Kumasaka, S;Kashima, I
在这项研究中，从数字腰椎图像中获得骨特征参数，以确定它们在评估二元小梁骨骼模式中的潜在用途。该系统由放大射线照相技术、计算机射线照相 (CR)、微聚焦 X 射线计算机断层扫描 (mu-CT) 和数学形态学图像处理组成。使用放大十倍的腰椎松质骨块 (1.5 x 1.5 x 1.5 cm(3)) 生成数字 CR 图像。然后对这些信息进行数学形态学图像处理，随着操作次数（n）的增加，提取出八种具有不同连续性的二元小梁骨骼图案。这些骨骼图案用作测试图案，并通过计算骨骼像素百分比（SKP）和星体积分析（Vsk，骨骼小梁元素的体积；Vsp，非骨骼元素的体积）进行定量分析。然后使用通用个人计算机将二元骨骼模式数据转换为mu-CT格式，并使用mu-CT内置图像分析软件对图像进行微观结构指数、分形维数分析和节点支柱分析的定量分析。随着 n 的增加，SKP 和 Vsk 显示出预期的下降。同时，随着 n 的增加，Vsp 显示出预期的增加。骨骼厚度（Sk.Th）在n=1~4和it=5~7时呈现恒定值，但在n=1和n=5时呈阶梯式下降。骨骼数量（Sk.N）、骨面积分数（B.Ar/T.Ar）和骨周长分数（B.Pm/T.Ar）随着增加而减少。骨骼空间 (Sk.Sp) 和周长与面积之比 (B.Pm/B.Ar) 随着 n 的增加而增加。在分形维数分析中，该值随着n的增加而减小，并且变化与图像观察类似。事实上，SKP、星体积分析、Sk.N、B.Ar/T.Ar、B.Pm/T.Ar、Sk.Sp 和 B.Pm/B.Ar 都密切反映了图像的观测分析。可以定量地证明骨骼结构作为以下参数的函数的联系。使用的参数是节点（Nd）和终点（Tm）。总支柱长度（TSL）和锡的变化可以证明骨骼结构的定量变化。这些结果表明，由放大射线照相技术、CR、mu-CT和数学形态学图像处理相结合组成的系统可能是定量骨骼结构分析和腰椎结构评估的有用工具，用于评估骨骼结构变化。为所需的结构变化选择合适的参数非常重要。
In this study, bone feature parameters were obtained from digital lumbar vertebral images to determine their potential usefulness in assessing binary trabecular skeletal patterns. The system consisted of a magnification radiographic technique, computed radiography (CR), microfocused X-ray computed tomography (mu-CT), and mathematical morphology image processing. A digital CR image was produced, using a ten-times magnified lumbar vertebral cancellous bone block (1.5 x 1.5 x 1.5 cm(3)). The information was then subjected to mathematical morphology image processing, to extract eight binary trabecular skeletal patterns having different continuities as the operation number (n) increased. These skeletal patterns were used as test patterns and analyzed quantitatively by calculation of the skeletal pixel percentage (SKP) and star volume analysis (Vsk, volume of skeletal trabecular elements; Vsp, volume of nonskeletal elements). Then the binary skeletal pattern data were converted into the mu-CT format, using a general personal computer, and the images were quantitatively analyzed with mu-CT built-in image analysis software for microstructural indices, fractal dimension analysis, and node-strut analysis. The SKP and Vsk showed an expected decrease as n increased. Concurrently, the Vsp showed an expected increase as n increased. The skeletal thickness (Sk.Th) showed a constant value at n = 1 to 4 and it = 5 to 7, but decreased in a stepwise fashion at n = 1 and n = 5. The skeletal number (Sk.N), bone area fraction (B.Ar/T.Ar) and bone perimeter fraction (B.Pm/T.Ar), decreased as it increased. Skeletal space (Sk.Sp) and perimeter-to-area ratio (B.Pm/B.Ar) increased with increasing n. In the fractal dimension analysis, the values decreased with increasing n and changes similar to the image observations. In fact, the SKP, star volume analysis, Sk.N, B.Ar/T.Ar, B.Pm/T.Ar, Sk.Sp, and B.Pm/B.Ar all closely mirrored the observational analysis of the images. Linkage of the skeletal structure as functions of the following parameters could be quantitatively demonstrated. The parameters used were node (Nd) and terminus (Tm). Variations in the total strut length (TSL) and Tin could demonstrate quantitative changes in skeletal structure. These results indicate that the system consisting of a combination of a magnification radiographic technique, CR, mu-CT, and mathematical morphology image processing may be a useful tool for quantitative skeletal structure analysis and the structural assessment of lumbar vertebrae, for the assessment of skeletal structural changes. It is important to choose suitable parameters for the desired structural changes.