Abstract
The study of biology and population dynamics of fish species requires the estimation of fecundity in individual fish in a routine way in many fisheries laboratories. The traditional procedure used by fisheries research is to count the oocytes manually on a subsample of known weight of the ovary, and to measure few oocytes under a binocular microscope. This process could be done on a computer using an interactive tool to count and measure oocytes. In both cases, the task is very time consuming, which implies that fecundity studies are rarely conducted routinely. This work represents the first attempt to design an automatic algorithm to recognize the oocytes in histological images. Two approaches based on region and edge information are described to segment the image and extract the oocytes. An statistical analysis reveals that higher than 74% of oocytes are recognized for both approaches, when an overlapping area between machine detection and true oocyte demanded is greater than 75%.
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Alén, S., Cernadas, E., Formella, A., Domínguez, R., Saborido-Rey, F. (2006). Comparison of Region and Edge Segmentation Approaches to Recognize Fish Oocytes in Histological Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_77
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DOI: https://doi.org/10.1007/11867661_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
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