Building shape and texture models of diatoms for analysis and synthesis of drawings and identification

We describe tools for automatic identification of diatoms by comparing their photographs with other photographs and drawings, via a model. Identification of diatoms, i.e. assigning a new specimen to one of the known species, has applications in many disciplines, including ecology, paleoecology and forensic science. The model we build represents life cycle and natural variation of both external shape and internal texture over multiple species and is based on principal curves. The model is also suitable for automatically producing drawings of diatoms at any stage of their life cycle development. Similar drawings are traditionally used for diatom identification, and encapsulate visually salient diatom features. In this article we describe the methods used to analyse photographs and drawings, present our model of diatom shape and texture variation, and illustrate our approach with a collection of drawings synthesised from our model and derived from example photographs. Finally, we present the results of identification experiments using photographs and drawings

Heroic failure or new dawn? Image-based identification of microalgae

A defensible estimate of species numbers in diatoms is c. 200,000, and evidence from several other microalgal groups indicate that current taxonomies are often ‘coarse-grained’ and do not discriminate sufficiently between biologically significant entities. However, making taxonomies more precise can make them difficult to use. Molecular methods offer one way around this problem and it has been suggested that sequence data could be used to ‘bar-code’ species for identification. However, this is currently unrealistic for many microalgae, because of rarity, poor sampling, recalcitrance in culture, or difficulty in obtaining sequences; furthermore, diatoms need to be identified when dead, for palaeoecology. The ADIAC and DIADIST projects were developed to make better use of morphological information, extracted without supervision from digital images, in classification and identification. Some new shape descriptors are highly sensitive and appear to surpass human visual capacity. DIADIST emulates traditional drawing in that a complex image is reduced to quantified, diagnostic essentials, which are then used for matching against a database of digitized drawings or photographs. DIADIST methods being developed can detect and represent striation patternsencouragingly well. Successful identification rates of > 95% in tests of ADIAC algorithms compare favourably with those achieved by experts. [EU, BBSRC funding]

New methods for preparing, imaging and typifying desmids (Chlorophyta, Zygnematophyceae), including extended depth of focus and 3-D reconstruction

Species- and genus-level taxonomy of desmids depends largely on shape and detail of the cell wall and chloroplast morphology. The depth of most desmid semicells, relative to the focal depth of conventional light microscopes, means that morphological characteristics are usually illustrated by drawings, made from material that is mounted in water to allow reorientation of specimens to different aspects of shape and pattern. Though a productive approach for two centuries, this has the disadvantages that features not initially detected or thought irrelevant are not recorded, drawing quality is variable, and individual specimens are rarely retained for further study. We describe methods for making permanent preparations of desmid cell walls and using these to produce extended depth of focus summary images and three-dimensional (3-D) reconstructions. Together with World-Wide Web dissemination of image stacks, these advances make it practical to make a desirable change from typification via drawings to typification via single or multiple preserved specimens. They will also facilitate standardization of taxon concepts and identification

Diatom contour analysis using morphological curvature scale spaces

A method for shape analysis of diatoms (single-cell algae with silica shells) based on extraction of features on the contour of the cells by multi-scale mathematical morphology is presented. After building a morphological contour curvature scale space, we present a method for extracting the most prominent features by unsupervised cluster analysis. The number of extracted features matches well with those found visually in 92 % of the 350 diatom images examined