EurOCEAN 2000 - Vlaams Instituut voor de Zee

SLIDE SCANNING FOR DIATOM LOCALIZATION
Partner CSIC is working on this challenging problem by combining analyses at
different magnifications. One problem is that slides may contain debris and
broken or overlapping diatoms. Cairns et al. (1972) have proposed
identification methods based on coherent optics and holography, but they did
not consider the problem of the automation at low resolutions. Culverhouse et
al. (1996) developed methods for phytoplankton identification using neural
networks, but again they did not consider a fully automatic method. To the best
of our knowledge, ADIAC includes the first approach to automate the image
acquisition process. The algorithm consists of three steps:
1) Image analysis at a low magnification (e.g. 5x). At this resolution it is impossible to
discriminate between diatoms and other objects, but this step only serves to extract
possible candidate positions to be analysed at a higher magnification. The background is
suppressed by a top-hat filter that also enhances amplitudes at edges, dots or lines. Then
the image is binarised by histogram thresholding.
2) Stage micro-positioning. The positions of centroids provide the information to move the
stage. Each particle is found using an automatic outlining technique. The (x,y) coordinates
are given by the centroid of the best-fitting parallellogram, measured from the upper-left
corner of the image. The particles are sorted according to their size. Once sorted, the
coordinates are sent to the serial port of the stage controller after increasing the
magnification.
3) Autofocusing. Here the requirements are speed, sharpness and robustness to noise. The
Tenengrad method is considered to be one of the best. Recently other focus measures
based on a modified Laplacian method have been published. CSIC has developed two
improved methods by modifying the Tenengrad and Laplacian ones. Measurements show
that they outperform the existing methods (Pech-Pacheco et al., 2000).
RECOGNITION AND CLASSIFICATION
The University of Algarve (UALG) is developing two strategies for a contour-based
identification. In the first one, an ellipse is dynamically fitted to the contour until it covers the
elliptical (central) part. Then a characteristic signal is extracted on the basis of the contour
points and their distances to the ellipse foci. This signal is zero where the ellipse covers the
contour, and unequal zero elsewhere, for example at the two valve endings in the case of
pennate diatoms. These deviations from zero allow to characterise the shape in terms of
symmetry type and end type, and provide a syntactical description that is interpretable by
diatomists. First results showed that a correct hit rate beyond 90% can already be achieved,
although the method has room for refinements. The method for the dynamic ellipse fitting will
be published soon (Ciobanu et al., 2000a) and a full paper with identification tests is in
preparation (Ciobanu et al., 2000b).
A second and new UALG method is based on a multiscale line/edge representation of the
characteristic contour signal in the complex Gabor scale space in which (1) the signal is filtered
with 360 complex Gabor kernels, (2) lines and edges are detected at all scales, (3) a stability
analysis over neighbouring scales is applied and (4) features in terms of initial and final scales
and amplitudes of the lines and edges are extracted. This method also allows for a syntactical
analysis by diatomists in terms of shape features and species-membership confidence. First
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