Data applied to automatic method to transform routine otolith images for a standardized otolith database using R
|Author(s)||Andrialovanirina Nicolas1, 2, Hache Alizee1, Mahe Kelig1, Couette Sébastien3, Poisson Caillault Emilie2|
|Affiliation(s)||1 : Ifremer, Laboratoire Ressources Halieutiques, 150 quai Gambetta, BP 699, 62321 Boulogne-sur-Mer, France
2 : LISIC, Université Littoral Côte d’Opale (ULCO), 62228 Calais, France
3 : Ecole Pratique des Hautes Etudes, PSL, Paris & UMR Biogéosciences, université de Bourgogne, 21000 Dijon, France
|Keyword(s)||Otoliths, Image processing, Standardization, Rotation, Shape analysis, Binarization|
Fisheries management is generally based on age structure models. Thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. The otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. It contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. The traditional human reading method to determine age is very time-consuming. Automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. Otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. Using the routinely measured otolith data of plaice (Pleuronectes platessa; Linnaeus, 1758) and striped red mullet (Mullus surmuletus; Linnaeus, 1758) in the eastern English Channel and north-east Arctic cod (Gadus morhua; Linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. Contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. To finalize this standardization process, all images were resized and binarized. Several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. For this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). This method was approved to these three species and could be applied for others species for age determination and stock identification.
|Acknowledgements||This study was supported by the Data Collection Framework (DCF; EC Reg. 199/2008, 665/2008; Decisions 2008/949/EC and 2010/93/EU). This work was supported by the Institut Français de Recherche et d’Exploitation de la Mer and the ULCO University (doctoral support to N. ANDRIALOVANIRINA, 2021-2024).|
The database for routine otolith processing has two components: firstly, the images (using reflected light with scanner or binocular dissecting microscope) database with a name which is composed of the sampling quarter and year, the name of the reader, and the location (i.e. survey name or fishing port); secondly, the metadata file (only the plaice database) which includes the name of the otolith image and the metadata on the fish catch (survey/commercial sampling, date, location, vessel, and gear) and species (sex, sexual maturity stage, length, and weight).