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Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates
Friedland, K.D.; Ama-Abasi, D.; Manning, M.; Clarke, L.; Kligys, G.; Chambers, R.C. (2005). Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. J. Sea Res. 54(4): 307-316.
In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101, more
Peer reviewed article  

Available in Authors 

    Egg counters; Eggs; Fecundity; Fishery management; Image processing; Population dynamics; Alosa sapidissima (Wilson, 1811) [WoRMS]; Marine

Authors  Top 
  • Friedland, K.D.
  • Ama-Abasi, D.
  • Manning, M.
  • Clarke, L.
  • Kligys, G.
  • Chambers, R.C.

    The production of eggs in a fish population is a fundamental parameter in fisheries management and ecology. Management decisions are based largely on the abundance and composition of the spawning stock; hence it is essential to estimate the contribution of viable eggs by females of various ages, which may depend on the size and maturation schedules in females of younger ages, and the size and reproductive senescence of older ones. The level of recruitment may also be influenced by the size and quality of eggs. Egg quality can be characterised in a number of ways; however, the most useful methods are those that are efficient and widely available. Estimating potential fecundity and egg size in fish and invertebrate populations has been hindered by the processing time, toxicity, and resources required by traditional methods. We have developed an imaging-based technique that counts and measures oocytes from a gravimetric gonadal sub-sample in relatively little time and at low cost. Sub-samples were preserved in a non-toxic formulation of Gilson's solution, which offers an alternative to other preservatives commonly used in fecundity studies. The technique uses high-resolution optical scans of plated oocytes, imaging software, and user-defined object classifications to separate oocyte from ancillary material likely to be present in a processed sample. Estimates of misclassification are as low as 1% (false-negatives) in automated counts.

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