Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth.

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.

A Bayesian network model for the diagnosis of the caring procedure for wheelchair users with spinal injury.

This paper describes a probabilistic causal model for the caring procedure to be followed on wheelchair users with spinal injury. Due to loss of sensation and movement caused by spinal cord injuries, the information extracted about patient findings (i.e. the signs and symptoms) can often be incomplete. This, in turn, introduces uncertainty in assessing the existence and severity of a given condition-and thus, employment of the appropriate caring procedure. Bayesian networks are a framework that enables probabilistic inference; therefore, they are useful for diagnostic reasoning and selection of the appropriate caring procedure in the face of uncertainty. The network structure and numerical parameters are based on data elicited from the qualified staff nurses and available literature of the National Spinal Injury Centre, Stoke Mandeville Hospital, Aylesbury, UK, as well as the compiled knowledge base within the DIMITRA rule-based expert system [M. Athanasiou, J.Y. Clark, DIMITRA: an online expert system for carers of paraplegics and quadriplegics, International Journal of Healthcare Technology and Management 7(5) (2006) 44-451]. We also present the model and report the results of the diagnostic performance tests using the AgenaRiskn [Agena Limited, AgenaRisk Software Package, http://www.agena.co.uk] Bayesian network package.

Artificial neural networks for species identification by taxonomists.

This paper is a study of the value of applying artificial neural networks (ANNs), specifically a multilayer perceptron (MLP), to identification of higher plants using morphological characters collected by conventional means. A practical methodology is thus demonstrated to enable botanical or zoological taxonomists to use ANNs as advisory tools for identification purposes. A comparison is made between the ability of the neural network and that of traditional methods for plant identification by means of a case study in the flowering plant genus Lithops N.E. Brown (Aizoaceae). In particular, a comparison is made with taxonomic keys generated by means of the DELTA system. The ANN is found to perform better than the DELTA key generator, for conditions where the available data is limited, and species relatively difficult to distinguish.