Swarming around Shellfish Larvae Digital Images
53. 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.
Figure - On the left a compendium
of 9 raw images (out of 20 samples) used in the present study.
Respective segmented images on the rigth.
PDF file: paper (503 Kb)
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).
59. Self-Regulated Artificial Ant Colonies on Digital Image Habitats.
PDF file: paper (503 Kb)
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-based Clustering, Swarm Intelligence, Stigmergy,
Computer-assisted Taxonomy.
Cited
by:
º
Walther
Fledelius, "Swarm based Medical Image Analysis: Applied In-Vivo Corneal
Confocal Microscopy", PhD Thesis, Aarhus University, Faculty of Health
Sciences, Denmark, 2007.
31. Map
Segmentation by Colour Cube
Genetic K-Mean
Clustering.
70. Computational
Chemotaxis
in Ants and Bacteria
over Dynamic
Environments.
26. Image
Colour Segmentation by
Genetic Algorithms.
59. Self-Regulated Artificial Ant Colonies on Digital Image Habitats.