Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks
Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks
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DOI：
10.1017/s1431927618015428
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发表时间：
2018-12-01
影响因子：
2.8
通讯作者：
Moebis, Anja
中科院分区：
文献类型：
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作者：
Lormand, Charline;Zellmer, Georg F.;Moebis, Anja
火山岩中的晶体记录了喷发前岩浆演化过程中的地球化学和结构特征。这一岩浆历史的线索可以通过晶体粒度分布（CSD）研究来检验。CSD的分析是一种标准的岩石学工具，但由于手工绘制晶体边缘而很费力。ImageJ中的可训练Weka分割（TWS）插件是一个很有前途的替代方案。它使用机器学习和图像分割来对图像进行分类。我们记录了背散射电子（BSE）图像的三个火山样品具有不同的结晶度（35，50和>= 85体积%），使用扫描电子显微镜（SEM）的可变图像分辨率，然后我们测试使用TWS。从自动分割的图像获得的晶体测量值与手动分割的晶体测量值进行比较。高达50体积%的结晶度的样品成功分割使用TWS。在显著更高的堆叠下的分割失败，因为不能区分晶体边界。TWS分类器的准确性性能测试产生高的F-分数（>0.930），因此，TWS是一个成功的和快速的计算工具，从玻璃质岩石的BSE图像概述晶体。最后，可靠的CSD的可以使用低成本的桌面扫描电镜，铺平了道路，利用这种新的岩石学方法的广泛的研究。
Crystals within volcanic rocks record geochemical and textural signatures during magmatic evolution before eruption. Clues to this magmatic history can be examined using crystal size distribution (CSD) studies. The analysis of CSDs is a standard petrological tool, but laborious due to manual hand-drawing of crystal margins. The trainable Weka segmentation (TWS) plugin in ImageJ is a promising alternative. It uses machine learning and image segmentation to classify an image. We recorded back-scattered electron (BSE) images of three volcanic samples with different crystallinity (35, 50 and >= 85 vol. %), using scanning electron microscopes (SEM) of variable image resolutions, which we then tested using TWS. Crystal measurements obtained from the automatically segmented images are compared with those of the manual segmentation. Samples up to 50 vol. % crystallinity are successfully segmented using TWS. Segmentation at significantly higher crystallinities fails, as crystal boundaries cannot be distinguished. Accuracy performance tests for the TWS classifiers yield high F-scores (>0.930), hence, TWS is a successful and fast computing tool for outlining crystals from BSE images of glassy rocks. Finally, reliable CSD's can be derived using a low-cost desktop SEM, paving the way for a wide range of research to take advantage of this new petrological method.