计算机断层扫描马尾松缺陷及其图像解译

刘晨君; 杨淑敏; 薛紫荞; 尚莉莉; 刘杏娥; 王卿平; 赵德达

doi:10.12171/j.1000-1522.20240007

计算机断层扫描马尾松缺陷及其图像解译

刘晨君^1,,
杨淑敏^1, ,,
薛紫荞¹,
尚莉莉¹,
刘杏娥¹,
王卿平^{1, 2},
赵德达^{1, 3}

1.
国际竹藤中心，国家林业和草原局/北京市共建竹藤科学与技术重点实验室，北京 100102
2.
北华大学材料科学与工程学院，吉林长春132013
3.
中国农业科学院农业信息研究所，北京 100081

基金项目: 国家重点研发计划项目（2022YFD2200901）。

详细信息

作者简介:
刘晨君。主要研究方向：竹藤等生物质材料。Email：lcj_197618@163.com　地址：100102 北京市朝阳区国际竹藤中心

责任作者:
杨淑敏，研究员。主要研究方向：竹藤等生物质材料。Email：yangsm@icbr.ac.cn　地址：同上。

中图分类号: S781.5；O434.1
计量
- 文章访问数: 192
- HTML全文浏览量: 25
- PDF下载量: 23
出版历程
- 收稿日期: 2024-01-04
- 修回日期: 2024-09-15
- 录用日期: 2024-09-29
- 网络出版日期: 2024-10-06
- 刊出日期: 2024-10-24

Computerized tomography of defects in Pinus massoniana and its image interpretation

1.
International Centre for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration/ Beijing for Bamboo & Rattan Science and Technology, Beijing 100102, China
2.
College of Materials Science and Engineering, Beihua University, Changchun 132013, Jilin, China
3.
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China

摘要

摘要:
目的
通过解译计算机断层扫描（CT）图像获取马尾松原木的缺陷类型、空间分布和形态特征，以实现目标缺陷的三维重建，为CT技术在木质材料中的应用提供技术支撑。
方法
设置合理的扫描参数快速完成马尾松原木扫描。针对CT扫描获取的马尾松原木缺陷图像，采用曲率各向异性扩散滤波去除噪声，选取合理阈值进行阈值分割，实现裂纹、虫眼的分割；采用参数形变模型算法，实现节子的分割。利用Mimics软件，通过三维区域生长法建立裂纹、虫眼的三维模型；基于MITK Workbench软件，通过VTK三维可视化面绘制中的移动立方体算法建立节子的三维模型。
结果
阈值分割法、参数形变模型法能快速准确地识别原木内部缺陷并提取缺陷特征信息，具有较高的识别效率和精度，相对误差较小，在3.63% ~ 10.36%范围内波动。三维区域生长法和移动立方体算法实现了原木内部裂纹、虫眼和节子的三维重建，能够快速获取缺陷的空间分布、结构特征，并量化特征信息。
结论
本研究提出的分析方法适用于不同缺陷的图像分割和三维可视化，可以获取缺陷在马尾松原木中的空间分布位置与特征参数，缺陷检测准确率高。研究结果为木材保护和木材利用的改进提供了技术支撑。
- 缺陷 /
- 断层扫描 /
- 节子 /
- 图像分割 /
- 三维 /
- 可视化 /
- 算法
Abstract:
Objective
In order to realize the three-dimensional reconstruction of targeted defects and provide technical support for the application of CT technology in wood materials, the defect types, spatial distribution and morphological characteristics of Pinus massoniana log were interpreted based on computerized tomography (CT) images.
Method
Reasonable parameters were set to quickly complete the scanning of P. massoniana logs. For CT image of the defects in P. massoniana log, the curvature anisotropic diffusion filtering was used to reduce noise. The threshold segmentation was applied to realize the segmentation of crack and insect hole by selecting reasonable thresholds, and the parametric deformation modeling algorithm was used to realize the segmentation of knots. Using Mimics software, the three-dimensional model of crack and insect hole was established by 3D area growth method. Based on MITK Workbench software, the 3D model of knots was established using the moving cube algorithm in 3D visualization surface rendering of VTK.
Result
The threshold segmentation method and parameter deformation modeling method can quickly and accurately identify the internal defects of logs and extract the defect feature information, which had high identification efficiency and accuracy. The relative error was small, fluctuating within the range of 3.63%−10.36%. The three-dimensional reconstruction of internal cracks, insect holes and knot was realized by three-dimensional region growth method and the moving cube algorithm, which can quickly obtain the spatial distribution, structural characteristics and quantify the feature information of defects.
Conclusion
An image segmentation and three-dimensional visualization method suitable for different defects is proposed, the spatial distribution location and characteristic parameters of the defects in P. massoniana logs are obtained, and the accuracy of defect detection is high. The research results can provide technical support for wood protection and utilization improvement.
- defect /
- tomography /
- knot /
- image segmentation /
- three dimensional /
- visualization /
- algorithms

HTML全文

图 1 预处理前后的马尾松缺陷 CT图像

红圈为节子，蓝圈为虫眼，黄圈为裂纹。The red circle means knot, the blue circle means insect hole, the yellow circle means crack.

Figure 1. CT images of defects in Pinus massoniana before and after pretreating

下载: 全尺寸图片幻灯片

图 2 不同区域的马尾松缺陷CT图像和灰度直方图

灰度直方图对应CT图像中的红线处。The grayscale histogram corresponds to the red line in the CT image.

Figure 2. CT images of defects in Pinus massoniana and grayscale histograms in different regions

下载: 全尺寸图片幻灯片

图 3 参数形变模型算法分割后马尾松节子CT图像

b和c分别为a中沿绿线、蓝线剖开的纵剖面。b and c are the longitudinal sections dissected along the green line and the blue line in a, respectively.

Figure 3. CT images of knots in Pinus massoniana after segmentation by parametric deformation modeling algorithm

下载: 全尺寸图片幻灯片

图 4 马尾松缺陷CT图像的分割

黄圈为裂纹，蓝圈为虫眼。The yellow circle means crack, the blue circle means insect hole.

Figure 4. CT image segmentation of defects in Pinus massoniana

下载: 全尺寸图片幻灯片

图 5 马尾松裂纹和虫眼的三维重建视图

绿色为裂纹，蓝色为虫眼，黄色为原木轮廓。The green means crack, the blue means insect hole, and the yellow means log outline.

Figure 5. Three-dimensional reconstruction view of cracks and insect holes in Pinus massoniana

下载: 全尺寸图片幻灯片

图 6 马尾松节子CT图像分割提取

b和c分别为a中沿绿线、蓝线剖开的纵剖面。b and c are the longitudinal sections dissected along the green line and the blue line in a, respectively.

Figure 6. CT image segmentation and extraction of a knot in Pinus massoniana

下载: 全尺寸图片幻灯片

图 7 马尾松节子三维重建视图

Figure 7. Three-dimensional reconstruction view of knots in Pinus massoniana

下载: 全尺寸图片幻灯片

图 8 马尾松缺陷实测与CT测定面积比较

Figure 8. Comparison of measured area and CT tested area of defects in P. massoniana

下载: 全尺寸图片幻灯片

表 1 马尾松原木各部位平均CT值

Table 1 Average CT values of different parts in P. massoniana log

序号 No.	CT值 CT value
序号 No.	心材 Heartwood	边材 Sapwood	节子 Knot
1	−229.53	−351.20	157.10
2	−391.25	−369.50	165.00
3	−358.15	−391.10	172.60
4	−320.60	−373.75	174.42
均值 Average value	−324.88	−371.38	167.28

下载: 导出CSV

表 2 马尾松原木中裂纹体积参数值

Table 2 Volumetric parameter values of cracks in P. massoniana log

序号 No.	裂纹体积 Volume of crack/cm³	原木体积 Volume of log/cm³	裂纹占原木体积百分比 Percentage of cracks in volume of log/%
1-1	1.78	6 143.05	0.03
1-2	2.98	6 143.05	0.05
1-3	1.65	6 143.05	0.03
1-4	2.07	6 143.05	0.03
1-5	1.18	6 143.05	0.02
2-1	2.81	9 783.06	0.03
2-2	6.49	9 783.06	0.06
2-3	2.02	9 783.06	0.02
2-4	1.28	9 783.06	0.01

下载: 导出CSV

表 3 马尾松原木中虫眼体积参数值

Table 3 Volumetric parameter values of insect holes in P. massoniana log

序号 No.	虫眼体积 Volume of insect hole/cm³	原木体积 Volume of log/cm³	虫眼占原木体积百分比 Percentage of insect holes in volume of log/%
1-1	1.98	6 143.05	0.03
1-2	1.17	6 143.05	0.02
1-3	1.07	6 143.05	0.02
1-4	4.31	6 143.05	0.07
1-5	1.37	6 143.05	0.02
1-6	1.96	6 143.05	0.03
1-7	0.78	6 143.05	0.01
1-8	1.57	6 143.05	0.03
2-1	1.02	9 783.06	0.01
2-2	2.33	9 783.06	0.02
2-3	1.76	9 783.06	0.02
2-4	1.66	9 783.06	0.02
2-5	0.80	9 783.06	0.01
2-6	2.77	9 783.06	0.03
2-8	0.80	9 783.06	0.01

下载: 导出CSV

表 4 马尾松原木中节子体积参数值

Table 4 Volumetric parameter values of knots in P. massoniana log

序号 No.	节子数量 Knot number	节子体积 Knot volume/cm³	原木体积 Log volume/cm³	节子占原木体积百分比 Percentage of knots in volume of log/%
1	7	310.00	8 055.75	3.84
2	6	212.10	5 857.49	3.62
3	9	266.44	9 201.68	2.89
4	4	361.64	1 0173.00	3.55
5	5	148.70	6 304.89	2.35

下载: 导出CSV

表 5 节子、裂纹和虫眼的CT测定与实测面积比较

Table 5 Comparison of the measured area of knots, cracks and insect holes with the area tested by CT

类型 Type	试样编号 Specimen No.	横截面半径 Cross-sectional radius/mm	横截面面积 Cross-sectional area/mm²	实测面积 Measured area/mm²	CT测定面积 Area tested by CT/mm²	相对误差 Relative error/%
节子 Knot	4-1	85.37	22 884.17	788（3.44%）	857.51（3.75%）	8.82
	4-2	108.71	37 109.63	1 029（2.77%）	1 113.42（3.00%）	8.20
	4-3	108.71	37 109.63	941（2.54%）	975.13（2.63%）	3.63
裂纹 Carck	5-1	71.30	15 962.79	802（5.02%）	763.12（4.54%）	−4.85
	5-2	83.50	21 892.87	857（3.91%）	790.62（3.61%）	−7.75
	5-3	62.00	12 070.16	292（2.42%）	274.51（2.27%）	−5.99
	5-4	61.75	11 973.02	300（2.51%）	271.14（2.26%）	−9.62
虫眼 Insect hole	6-1	87.25	23 903.45	1 805（7.55%）	1 617.98（6.77%）	−10.36
虫眼 Insect hole	6-2	57.00	10 201.86	639（6.26%）	698.71（6.08%）	9.34
注：括号内数值为缺陷面积在横截面面积中的占比。Note: values inside the parentheses are the proportion of defect area to the cross-sectional area.

下载: 导出CSV

参考文献(15)

[1]	Kaiser S, Kaiser M S. Comparison of wood and knot on wear behaviour of pine timber[J]. Research on Engineering Structures and Materials, 2020, 6(1): 35−44.
[2]	Kunesh R H, Johnson J W. Effect of single knots on tensile strength of 2-by 8- inch douglas-fir dimension lumber[J]. Forest Products Journal, 1972, 22: 32−37.
[3]	Mäkinen H. Effect of stand density on the branch development of silver birch (Betula pendula Roth) in central Finland[J]. Trees, 2002, 16: 346−353. doi: 10.1007/s00468-002-0162-x
[4]	钟丽辉, 程昱之, 孙永科, 等. 木材缺陷识别方法综述[J]. 农业技术与装备, 2020, 371(11): 151−152. doi: 10.3969/j.issn.1673-887X.2020.11.071 Zhong L H, Cheng Y Z, Sun Y K, et al. Review of wood defect identification method[J]. Agricultural Technology & Equipment, 2020, 371(11): 151−152. doi: 10.3969/j.issn.1673-887X.2020.11.071
[5]	张瑞峰, 夏坡坡. 基于CNN的典型木材缺陷图像识别研究[J]. 现代化农业, 2019(1): 37−40. doi: 10.3969/j.issn.1001-0254.2019.01.021 Zhang R F, Xia P P. Research on image recognition of typical wood defects based on CNN[J]. Modernizing Agriculture, 2019(1): 37−40. doi: 10.3969/j.issn.1001-0254.2019.01.021
[6]	Ling J X, Xie Y H. Research on wood defects classification based on deep learning[J]. Wood Research, 2022, 67(1): 147−156. doi: 10.37763/wr.1336-4561/67.1.147156
[7]	Wang B G, Yang C M, Ding Y C, et al. Detection of wood surface defects based on improved YOLOv3 algorithm[J]. BioRecsources, 2021, 16(4): 6766−6780. doi: 10.15376/biores.16.4.6766-6780
[8]	王正, 江莺, 严飞, 等. 基于YOLOv7的木材缺陷检测模型Wood-Net的研究[J]. 林业工程学报, 2024, 9(1): 132−140. Wang Z, Jiang Y, Yan F, et al. Research on wood defect detection model Wood-Net based on YOLOv7[J]. Journal of Forestry Engineering, 2024, 9(1): 132−140.
[9]	赵鹏, 赵匀, 陈广胜. 基于3D扫描技术的木材缺陷定量化分析[J]. 农业工程学报, 2017, 33(7): 171−176. doi: 10.11975/j.issn.1002-6819.2017.07.022 Zhao P, Zhao Y, Chen G S. Quantitative analysis of wood defect based on 3D scanning technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(7): 171−176. doi: 10.11975/j.issn.1002-6819.2017.07.022
[10]	何潇. 原木心边材密度与含水率的计算机断层扫描检测研究[D]. 哈尔滨: 东北林业大学, 2015. He X. Study on the log heartwood and sapwood density and moisture content detection based on computed tomography[D]. Harbin: Northeast Forestry University, 2015.
[11]	高明宇, 倪海明, 张博洋, 等. 一种基于 GoogLeNet 卷积神经网络的木节缺陷识别方法[J]. 森林工程, 2021, 37(4): 66−70. doi: 10.3969/j.issn.1006-8023.2021.04.009 Gao M Y, Ni H M, Zhang B Y, et al. A method for recognizing wood knots defects based on GoogLeNet convolutional neural network[J]. Forest Engineering, 2021, 37(4): 66−70. doi: 10.3969/j.issn.1006-8023.2021.04.009
[12]	Longuetaud F, Mothe F, Kerautret F, et al. Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples[J]. Computers and Electronics in Agriculture, 2012, 85: 77−89. doi: 10.1016/j.compag.2012.03.013
[13]	陈偲, 沈肇雨, 王正, 等. 正交胶合木平面剪切的开裂形貌及其破坏模式探究[J]. 林产工业, 2022, 59(6): 7−13. Chen S, Shen Z Y, Wang Z, et al. Study on plane shear cracking morphology and failure mechanism of cross laminated timber[J]. China Forest Products Industry, 2022, 59(6): 7−13.
[14]	王卿平, 刘杏娥, 张桂兰, 等. 基于X射线CT技术快速检测不同含水率状态下的毛竹密度[J]. 光谱学与光谱分析, 2016, 36(6): 1899−1903. Wang Q P, Liu X E, Zhang G L, et al. Rapidly detection for moso bamboo density under different moisture condition based on X-CT technology[J]. Spectroscopy and Spectral Analysis, 2016, 36(6): 1899−1903.
[15]	王亚磊, 赵茂程, 王正. 木材动态弹性模量测量中节子对于应力波传播的影响[J]. 西北林学院学报, 2014, 29(3): 183−187. doi: 10.3969/j.issn.1001-7461.2014.03.37 Wang Y L, Zhao M C, Wang Z. Influence of wood knots on the stress wave propagation in the dynamic measurement of elastic modulus[J]. Journal of Northwest Forestry University, 2014, 29(3): 183−187. doi: 10.3969/j.issn.1001-7461.2014.03.37

施引文献(10)

期刊类型引用(3)

1.	李辉，林沂，孟祥爽，史振伟，蔡万园. 基于地基激光雷达的栾树分形特征分析. 山东农业大学学报(自然科学版). 2022(03): 475-483 . 百度学术
2.	何东健，熊虹婷，芦忠忠，刘建敏. 基于多视角立体视觉的拔节期玉米水分胁迫预测模型. 农业机械学报. 2020(06): 248-257 . 百度学术
3.	郭彩玲，刘刚. 基于颜色取样的苹果树枝干点云数据提取方法. 农业机械学报. 2019(10): 189-196 . 百度学术