Automating Digital Leaf Measurement: The Tooth, the Whole Tooth, and Nothing but the Tooth
Automating Digital Leaf Measurement: The Tooth, the Whole Tooth, and Nothing but the Tooth
复制标题
DOI：
10.1371/journal.pone.0042112
复制
发表时间：
2012-08-01
期刊：
影响因子：
3.7
通讯作者：
Jin, Jing
中科院分区：
文献类型：
--
作者：
Corney, David P. A.;Tang, H. Lilian;Jin, Jing
许多种类的植物在叶子边缘有明显的齿。这些牙齿的存在和性质通常可以帮助植物学家识别物种。此外，人们早就知道，生活在寒冷地区的物种比生活在温暖地区的物种有更多的牙齿。因此，有人提出，树叶的化石遗迹可以作为古代气候重建的代表。对活植物的类似研究可以帮助我们理解它们之间的关系。所需的叶分析通常涉及相当多的手工工作，这在实践中限制了分析的叶的数量，潜在地降低了结果的有效性。在这项工作中，我们描述了一种新的算法来自动边缘牙齿分析在数字图像中发现的叶子。我们在从椴树（也称为椴树，椴树或椴树）收集的大量植物标本室标本上展示了我们的方法。我们选择了椴属，因为它的组成物种有不同大小和形状的齿状叶子。在之前的一项研究中，我们从一组c: 1100图像中自动提取了c: 1600片叶子。我们的新算法定位这些叶子边缘的牙齿，提取每颗牙齿的面积、周长和内角等特征，并对它们进行计数。我们根据人工分析的图像子集来评估算法的性能实现。结果表明，该算法的牙齿计数准确率为85%，牙齿面积估计准确率为75%。我们还证明了自动提取的特征足以使用简单的线性判别分析来识别不同种类的Tilia，并且与牙齿相关的特征是最有用的。
Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships. The required analysis of leaves typically involves considerable manual effort, which in practice limits the number of leaves that are analyzed, potentially reducing the power of the results. In this work, we describe a novel algorithm to automate the marginal tooth analysis of leaves found in digital images. We demonstrate our methods on a large set of images of whole herbarium specimens collected from Tilia trees (also known as lime, linden or basswood). We chose the genus Tilia as its constituent species have toothed leaves of varied size and shape. In a previous study we extracted c: 1600 leaves automatically from a set of c: 1100 images. Our new algorithm locates teeth on the margins of such leaves and extracts features such as each tooth's area, perimeter and internal angles, as well as counting them. We evaluate an implementation of our algorithm's performance against a manually analyzed subset of the images. We found that the algorithm achieves an accuracy of 85% for counting teeth and 75% for estimating tooth area. We also demonstrate that the automatically extracted features are sufficient to identify different species of Tilia using a simple linear discriminant analysis, and that the features relating to teeth are the most useful.