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Santiago de Compostela, Spain

Gonzalez-Rufino E.,University of Vigo | Carrion P.,University of Vigo | Cernadas E.,Research Center en Tecnoloxias da Informacion da | Fernandez-Delgado M.,Research Center en Tecnoloxias da Informacion da | Dominguez-Petit R.,CSIC - Institute of Marine Research
Pattern Recognition | Year: 2013

The estimation of fecundity and reproductive cells (oocytes) development dynamic is essential for an accurate study of biology and population dynamics of fish species. This estimation can be developed using the stereometric method to analyse histological images of fish ovary. However, this method still requires specialised technicians and much time and effort to make routinary fecundity studies commonly used in fish stock assessment, because the available software does not allow an automatic analysis. The automatic fecundity estimation requires both the classification of cells depending on their stage of development and the measurement of their diameters, based on those cells that are cut through the nucleous within the histological slide. Human experts seem to use colour and texture properties of the image to classify cells, i.e., colour texture analysis from the computer vision point of view. In the current work, we provide an exhaustive statistical evaluation of a very wide variety of parallel and integrative texture analysis strategies, giving a total of 126 different feature vectors. Besides, a selection of 17 classifiers, representative of the currently available classification techniques, was used to classify the cells according to the presence/absence of nucleous and their stage of development. The Support Vector Machine (SVM) achieves the best results for nucleous (99.0% of accuracy using colour Local Binary Patterns (LPB) feature vector, integrative strategy) and for stages of development (99.6% using First Order Statistics and grey level LPB, parallel strategy) with the species Merluccius merluccius, and similar accuracies for Trisopterus luscus. These results provide a high reliability for an automatic fecundity estimation from histological images of fish ovary. © 2013 Elsevier Ltd. All rights reserved. Source


Pintor J.M.,University of Vigo | Carrion P.,University of Vigo | Gonzalez-Rufino E.,University of Vigo | Formella A.,University of Vigo | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Govocitos is a multi-platform application designed to integrate all processes to estimate the fecundity of fish, a fundamental issue for the management of sustainable fisheries. Govocitos incorporates supervised and unsupervised algorithms to extract oocytes and their features in digitized histological images. Oocytes are classified automatically into the classes used traditionally in studies of reproductive ecology. A database gives support to allow for reproducible data management and to share results among different laboratories. The output of Govocitos has been evaluated through extensive validation procedures and found to be precise and accurate. Govocitos is open source software running on Linux and Windows platforms. © Springer International Publishing Switzerland 2015. Source

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