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Root length and diameter measurement using NIH Image: application of the line-intercept principle for diameter estimation

机译:使用NIH图像测量根长和直径:线截距原理在直径估计中的应用

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摘要

The objective of this study was to develop an image analysis algorithm for estimating the length versus diameter distribution of washed root samples. Image analysis was performed using a Macintosh computer and the public domain NIH Image program. After an appropriate binary image of roots was obtained, the image was processed to get the thinned image to calculate the length of the roots. The edge pixel of the binary image was then deleted and root length was calculated again. This `edge deletion–length calculation' cycle was repeated until no root pixel was left in the image. Repeated edge deletion removed one pixel layer from around the periphery of root objects in each iteration. The number of edge deletions, which is equivalent to the intercept length, can be used to estimate the root diameter. We used the vertical or horizontal intercept length, whichever was shorter. The accuracy of diameter estimation due to orientation of objects varied from 89.1 to 126.0%. Branching root systems consist of several orders of laterals, and as the root branches to a higher order, the diameter of the roots becomes smaller. Therefore, edge deletions eliminate sequentially from the highest order roots, which have the smallest diameter, to the lowest order roots, which have the widest diameter. Thus, the length and diameter of each root order can be calculated by the proposed method. For verification, images of copper wire of 0.23, 0.50, and 1.0 mm diameter were analyzed. The results showed reasonable agreement with the expected distribution of length versus diameter for randomly oriented objects, and consequently the wire length of each diameter could be estimated. The proposed method was tested for primary and secondary roots of water-cultured rice (Oryza sativa L.), and it was proven that the method can provide accurate length and diameter measurements for each root order.
机译:这项研究的目的是开发一种图像分析算法,以估计洗过的根样品的长度与直径分布。图像分析是使用Macintosh计算机和公共领域的NIH Image程序进行的。在获得合适的根的二值图像之后,对该图像进行处理以获得变细的图像以计算根的长度。然后删除二进制图像的边缘像素,并再次计算根长。重复此“边缘删除-长度计算”循环,直到图像中没有剩余根像素为止。重复的边缘删除在每次迭代中从根对象的外围删除了一个像素层。边缘缺失的数量等于截距长度,可用于估计齿根直径。我们使用垂直或水平截距,以较短者为准。由于物体定向而导致的直径估计的准确度从89.1%到126.0%不等。分支的根系由数个分支的侧枝组成,并且随着根向更高阶的分支,根的直径变小。因此,边缘删除从直径最小的最高阶根到直径最大的最低阶根依次消除。因此,每个根序的长度和直径可以通过提出的方法来计算。为了验证,分析了直径为0.23、0.50和1.0 mm的铜线图像。结果表明,对于随机定向的物体,其长度与直径的预期分布具有合理的一致性,因此可以估算每个直径的导线长度。对水培稻(Oryza sativa L.)的初生和次生根进行了测试,结果证明该方法可以为每个根序提供准确的长度和直径测量。

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