Data applied to automatic method to transform routine otolith images for a standardized otolith database using R

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.渔业管理通常以年龄结构模型为核心依据。据此，专家通过视觉判读流程分析钙化结构（鳞片、脊椎骨、鳍条、耳石（otolith）等），以此采集鱼类年龄鉴定数据。鱼类内耳中的耳石是应用最广泛的钙化结构，因其代谢惰性，且是历史上最早开发的年龄鉴定替代材料之一。耳石承载了鱼类整个生命周期的完整信息，可为所有商业鱼种的种群评估提供年龄结构数据。传统人工判读年龄的方法耗时极长，自动化图像分析可作为低成本替代方案，但首要步骤是将常规采集的耳石图像转换为数据库内的标准化图像，以便在年龄鉴定数据上应用机器学习技术。耳石形态由遗传禀赋与环境效应共同塑造，是识别种群单元的有效手段，因此标准化图像数据库可服务于该研究目标。本研究利用英吉利海峡东部的欧洲鲽（Pleuronectes platessa; Linnaeus, 1758）、条纹红鲻（Mullus surmuletus; Linnaeus, 1758）以及东北北极海域大西洋鳕鱼（Gadus morhua; Linnaeus, 1758）的常规耳石测量数据，从不同格式的原始图像中生成了灰度图像矩阵。随后通过轮廓检测算法识别破损耳石、单张图像内耳石的朝向以及图像中耳石的总数量。为完成该标准化流程，研究人员对所有图像进行了尺寸调整与二值化处理。基于这些标准化后的新图像，研究团队开发了多款数学形态学工具，用于图像对齐与朝向校正，使所有图像中的耳石布局保持统一。本研究使用了来自两个不同实验室的三个数据库，涵盖鳕鱼、欧洲鲽与条纹红鲻三个物种。该方法已在这三个物种上得到验证，可推广应用于其他物种的年龄判定与种群识别工作。