基于灰度及形态特征融合的Micro-CT图像纤维束识别方法研究
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摘要
利用Micro-CT成像以获得glass/epoxy(760E)编织复合材料的影像能够表征其织构形貌、内部缺陷,有助于研究复合材料微观结构与宏观力学性能之间的关系。但由于玻璃纤维与碳纤维在密度上与基体过于接近,导致Micro-CT图像中的灰度级差异过小且混乱,难以采用基于传统灰度特征的图像分割方法对纤维束所对应的图像区域进行识别。此外,由于制作工艺原因,正交两束纤维以及平行两束纤维过分挤压易造成工业CT图像中纤维图像区域相互混叠,加剧了对纤维束的准确识别的困难性。针对Micro-CT这种特殊的图像数据及纤维束区域分布相互混叠以及图像噪声的影响,采用多级图像处理及识别策略,将Micro-CT图像灰度特征及纤维束所表现出的形态特征进行融合,充分发挥灰度特征在噪声抑制及形态特征在纹理识别上的优势,并辅以形态学修正算法,实现了准确率较高的Micro-CT图像中纤维束识别。
The use of Micro-CT Imaging,for glass/epoxy(760 E)braided composites image,has the ability to characterize their texture morphology and internal defects,and contributes to the research on the relations between the microstructure and macro-mechanical properties of composites.However,the glass fiber and carbon fiber are excessively close to the matrix in terms of density,resulting in smaller difference and confusing of grayscale in Micro-CT images.Hence,it is difficult to recognize the fiber bundle by using the grayscale features.Moreover,two excessively-squeezed orthotropic fibers and two parallel fibers could easily lead to mutual aliasing in industrial CT fiber images,making it difficult to correctly recognize the fiber bundle.Considering the characteristics of the Micro-CT images,this article utilizes multi-level image processing and recognition strategy to integrate the morphological features exhibited by grayscale features of fiber bundles from.The method we proposed which can take full advantage of the gray-scale features in noise suppression and the morphological features in texture recognition,and also can recognize the fiber bundle from Micro-CT images with a higher accuracy.
The use of Micro-CT Imaging,for glass/epoxy(760 E)braided composites image,has the ability to characterize their texture morphology and internal defects,and contributes to the research on the relations between the microstructure and macro-mechanical properties of composites.However,the glass fiber and carbon fiber are excessively close to the matrix in terms of density,resulting in smaller difference and confusing of grayscale in Micro-CT images.Hence,it is difficult to recognize the fiber bundle by using the grayscale features.Moreover,two excessively-squeezed orthotropic fibers and two parallel fibers could easily lead to mutual aliasing in industrial CT fiber images,making it difficult to correctly recognize the fiber bundle.Considering the characteristics of the Micro-CT images,this article utilizes multi-level image processing and recognition strategy to integrate the morphological features exhibited by grayscale features of fiber bundles from.The method we proposed which can take full advantage of the gray-scale features in noise suppression and the morphological features in texture recognition,and also can recognize the fiber bundle from Micro-CT images with a higher accuracy.
引文
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[2]HADWIGER M,LAURA F,REZK-SALAMA C,et al.Interactive volume exploration for feature detection and quantification in industrial CT data.Visualization and Computer Graphics,IEEE Transactions on Visualization and Computer Graphics,2008,14(6):1507-1514.
[3]王慧倩,邹永宁,蔡玉芳,等.基于Hessian矩阵和熵的CT序列图像裂缝分割方法[J].仪器仪表学报,2016,37(8):1800-1807.
[4]NIELSEN M W,SCHMIDT J W,HGH J H,et al.Life cycle strain monitoring in glass fibre reinforced polymer laminates using embedded fibre Bragg grating sensors from manufacturing to failure[J].Journal of Composite Materials,2014,48(3):365-381.
[5]罗召洋,杨孝全,孟远征,等.Micro CT中混叠伪影的消除[J].物理学报,2010,59(11):8237-8243.
[6]董歌,罗守华,陈功.Micro CT投影图像噪声的去除[J].医疗卫生装备,2009,30(2):7-10.
[7]巩龙东,申秀丽.基于能量法的三维四向编织复合材料细观建模[J].航空动力学报,2015,30(1):106-113.
[8]HAMBLI R.Micro-CT finite element model and experimental validation of trabecular bone damage and fracture.Bone 2013;56(2):363-374.
[9]孙君顶,马媛媛.纹理特征研究综述[J].计算机系统应用,2010,19(6):245-250.
[10]ILYA STRAUMIT,STEPAN V.LOMOV,MAR-TINE WEVERS.Quantification of the internal structure and automatic generation of voxel models of textile composites from X-ray computed[C].Composites:Part A,2015,69:150-158.
[11]金铭,汪友生,边航.一种多特征融合的图像检索新方法[J].电子测量技术,2016,39(8):85-89.
[12]吴一全,孟天亮,吴诗婳.图像阈值分割方法研究进展20年(1994-2014)[J].数据采集与处理,2015,30(1):1-23.
[13]肖乐意,欧阳红林,范朝冬.基于记忆分子动理论优化算法的多目标截面投影Otsu图像分割[J].电子与信息学报,2018,40(1):189-199.
[14]苑玮琦,冯琪,柯丽.利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法[J].计算机应用研究,2009,26(8):3166-3168.
[15]朱贺,李臣明,张丽丽,等.联合灰度阈值分割及轮廓形态识别的河道提取[J].电子测量与仪器学报,2014,28(11):1288-1296.
[16]朱贺,李臣明,张丽丽,等.融合图模型及形态模型的SAR图像中河道提取[J].遥感技术与应用,2015,30(2):337-344.
[17]王延年,赵朗月,刘会敏.基于混合颜色空间的反应孔图像分割算法研究[J].国外电子测量技术,2017,36(12):20-23.
[18]潘春雨,卢志刚,秦嘉.基于区域阈值的图像分割方法研究[J].火力与指挥控制,2011,36(1):118-121,137.
[19]张振,陈哲,吕莉,等.基于视觉感受野的自适应背景抑制方法[J].仪器仪表学报,2014,35(1):191-199.