Shape identification and particles size distribution from basic shape parameters using ImageJ
Shape identification and particles size distribution from basic shape parameters using ImageJ
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DOI：
10.1016/j.compag.2008.02.007
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发表时间：
2008-10-01
影响因子：
8.3
通讯作者：
Methuku, S. R.
中科院分区：
文献类型：
--
作者：
Igathinathane, C.;Pordesimo, L. O.;Methuku, S. R.
在处理粒状或颗粒材料（包括尺寸减小）的各种技术领域中，需要快速且准确的粒度分布分析。我们开发了一个 ImageJ 插件，可以在识别不相交颗粒的形状后从数字图像中提取尺寸并确定其颗粒尺寸分布。我们确定 ImageJ 的长轴和短轴拟合椭圆以及开发的校正因子有效地确定了颗粒的尺寸。本文介绍了插件的开发及其在粮食和地面生物质中的应用。使用计算机生成的几何形状作为参考对象，开发了一种针对常见几何形状（例如正方形、倾斜正方形、长方形、倾斜矩形、圆形、椭圆形和倾斜椭圆形）的形状识别策略。该策略仅使用三个新定义的形状参数来识别对象，例如 ImageJ 生成的标准输出中的倒数长宽比、矩形度和费雷特长轴比。颗粒形状、尺寸和方向对与参考颗粒长度和宽度的偏差的影响的评估表明，所有这些因素的平均绝对偏差小于1.3%。开发的插件已成功应用于分析粮食和磨碎芒草颗粒图像的尺寸和尺寸分布。该插件可根据数字图像快速准确地生成颗粒尺寸分布，并可应用于各种颗粒分析应用。 (C) 2008 Elsevier B.V. 保留所有权利。
Quick and accurate particle size distribution analysis is desirable in various technical fields that handle granular or particulate materials including size reduction. We developed an ImageJ plugin that extracts the dimensions from a digital image of disjoint particles after identifying their shapes and determines their particles size distribution. We established that the major and minor axes of ImageJ fitted ellipse along with the developed correction factors efficiently determined dimensions of particles. This paper describes the plugin development and its application to food grains and ground biomass. Using computer generated geometrical shapes as reference objects, a shape identification strategy that addresses common geometric shapes such as square, inclined square, rectangle, inclined rectangle, circle, ellipse, and inclined ellipse was developed. The strategy used only three newly defined shape parameters to identify objects, such as reciprocal aspect ratio, rectangularity, and feret major axis ratio from the standard outputs generated by ImageJ. Evaluation of effects of the particles shape, size, and orientation on the deviation from the reference particle's length and width indicated that the mean absolute deviations of all these factors were less than 1.3%. Developed plugin was applied successfully to analyze the dimensions and size distribution of food grains and ground Miscanthus particles images. The plugin produced quick and accurate size distribution of particles from digital images and can be applied to variety of particle analysis applications.. (C) 2008 Elsevier B.V. All rights reserved.