Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth.
Corney, DP, Tang, HL, Clark, JY, Hu, Y and Jin, J (2012) Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth. PLoS One, 7 (8).
Available under License : See the attached licence file.
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 [Formula: see text] leaves automatically from a set of [Formula: see text] 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.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1371/journal.pone.0042112|
|Additional Information :||© 2012 Corney et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||09 Oct 2012 15:48|
|Last Modified :||23 Sep 2013 19:40|
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