E. Cernadas, P. Carrin, A. Formella, R. Domnguez, and F. Saborido-Rey (Spain)
Image analysis, classification, segmentation, fish oocytes,fecundity, histological images.
The study of biology and population dynamics of fish species requires the estimation of fecundity in individual fish in many fisheries laboratories. The traditional proce dure used by fisheries research is to count manually the oocytes on a subsample of known weight of the ovary, and to measure a few oocytes under a binocular microscope. This process can be done on a computer using an interac tive tool to count and measure oocytes. In both cases, the task is very time consuming, which implies that fecundity studies are rarely conducted routinely. We attempt to design a computer vision system which is able to recognize and classify the oocytes in a histolog ical image. The boundary of oocytes is detected using an algorithm based on edge information. Afterwards, oocytes are classified in cells with and without nucleus. A statisti cal evaluation of both stages reveals correct detection and classification of 65% when a 80% of overlap is demanded.
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