[Computational-biology] SELF-ORGANIZED COMPUTER-ASSISTED TAXONOMY: Swarming around Shellfish Larvae Images

Vitorino RAMOS vitorino.ramos at alfa.ist.utl.pt
Mon Dec 19 19:01:56 EST 2005


Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F.
Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in
WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India,
16-18 Dec., 2005.

URL: http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_53.html

PDF fie: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-WCLC05a.pdf

ABSTRACT: The collection of wild larvae seed as a source of raw
material is a major sub industry of shellfish aquaculture. To predict
when, where and in what quantities wild seed will be available, it is
necessary to track the appearance and growth of planktonic larvae. One
of the most difficult groups to identify, particularly at the species
level are the Bivalvia. This difficulty arises from the fact that
fundamentally all bivalve larvae have a similar shape and colour.
Identification based on gross morphological appearance is limited by
the time-consuming nature of the microscopic examination and by the
limited availability of expertise in this field. Molecular and
immunological methods are also being studied. We describe the
application of computational pattern recognition methods to the
automated identification and size analysis of scallop larvae. For
identification, the shape features used are binary invariant moments;
that is, the features are invariant to shift (position within the
image), scale (induced either by growth or differential image
magnification) and rotation. Images of a sample of scallop and
non-scallop larvae covering a range of maturities have been analysed.
In order to overcome the automatic identification, as well as to allow
the system to receive new unknown samples at any moment, a
self-organized and unsupervised ant-like clustering algorithm based on
Swarm Intelligence is proposed, followed by simple k-NNR nearest
neighbour classification on the final map. Results achieve a full
recognition rate of 100% under several situations (k =1 or 3).

KEYWORDS: Pattern Recognition, Biological automated Identification of
Shellfish Larvae, Colour Image Segmentation, Classification,
Self-Organized Ant-like Clustering, Swarm Intelligence,
Computer-assisted Taxonomy.



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