The study of biology and population dynamics of fish species requires the routine estimation of fecundity in individual fish 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 can be done on a computer using an interactive tool to count and measure oocytes. In both cases, thetask is very time consuming, which implies that fecundity studies are rarely conducted routinely. We attempt to design an algorithm being able to extract the oocytes in a histological image. In a previous work [1], a statistical comparison of the performance of region and edge segmentation approaches was presented. The results have been encouraging but the edge based approach needed to mark manually the centers of the cells of interest. This paper proposes a non–guided method to extract the cells of interest based on edge information. In a post–processing stage, the segmentation results of the region and edge approaches are combined to improve the performance. The rate of oocyte detection has been increased to 82% when the demanded overlap between machine detection and true oocyte area is greater than 75%

This investigation was supported by the Xunta de Galicia (regional government) projects PGIDIT04RMA305002PR and PGIDIT04RMA402003PR

6 pages, 4 figures

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