A new otolith image contour descriptor based on partial reflection

Reig-Bolaño, Ramon; Martí-Puig, Pere; Lombarte, Antoni; Soria, J.A.; Parisi-Baradad, Vicens

doi:10.1007/s10641-010-9700-3

Cited by 19 publications

(15 citation statements)

References 29 publications

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“…It has also been deemed to be an ideal natural marker for spatial stock, and is widely used in stock identification. The introduction of multiple image analysis techniques improves the ability to visualize and compare otolith shape, such as Fourier analysis [7,8], curvature scale space [9], wavelet transform [10, 11] and partial reflection [12]. However, few efforts have been put into the improvement of classification methods, whereas machine-learning methods (e.g.…”

mentioning

confidence: 99%

Population structure of Japanese Spanish mackerel Scomberomorus niphonius in the Bohai Sea, the Yellow Sea and the East China Sea: evidence from random forests based on otolith features

Zhang

et al. 2016

Fish Sci

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demographic properties, life history pattern and response to exploitation. Ignoring underlying spatial stock structure in a fishery management system can increase risk of local depletion, resulting in loss of genetic diversity [1][2][3]. Therefore, a thorough understanding of stock structure is the starting point of effective management in multi-stock fisheries [4,5].Otolith shape is subjected to a combination of genetic and environment effects and demonstrates stock-specific features [6]. It has also been deemed to be an ideal natural marker for spatial stock, and is widely used in stock identification. The introduction of multiple image analysis techniques improves the ability to visualize and compare otolith shape, such as Fourier analysis [7,8], curvature scale space [9], wavelet transform [10, 11] and partial reflection [12]. However, few efforts have been put into the improvement of classification methods, whereas machine-learning methods (e.g. random forests, artificial neural networks) have been applied in fish parasites and otolith microchemistry, and have outperformed discriminant analysis [13][14][15]. Discriminant analysis (linear, quadratic and canonical) is commonly used in population studies based on otolith shape, and multivariate normality and independence of the predictor variables are required for this method. Appropriate data transformations (log, square root, etc.) are often required to reach a normal distribution. As to multi-collinearity, there are two options: a principle component analysis or excluding variables with significant multi-collinearity. Algorithmic advantages of random forests enable researchers to eliminate the above steps, maximize the number of variables inputted for discrimination and retain the original information of each variable. However, an increase in classification accuracy has its own limits with the variables used, and many variables increase intrinsic classification accuracy but also decrease extrinsic accuracy in Abstract In this study, the population structure of Japanese Spanish mackerel Scomberomorus niphonius were investigated based on the geographical variation of otolith features, and the concept of random forests was introduced as a classifier. Samples were collected from eight main spawning grounds using commercial gill nets, covering the distribution range along the coast of China. Otolith shape was described by the shape variables and principle components of Fourier coefficients. Every possible combination of otolith variable was tested to search for an optimal variable combination. An intermediate number of variables (8 out of 13) produced the most discriminating signal, and then the eight grounds were divided into three stocks with 64.5 % global accuracy. The discriminant result provided phenotypic evidence for the common migration trajectory, and further confirmed the existence of a metapopulation in Japanese Spanish mackerel at a larger scale.

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confidence: 99%

Population structure of Japanese Spanish mackerel Scomberomorus niphonius in the Bohai Sea, the Yellow Sea and the East China Sea: evidence from random forests based on otolith features

Zhang

et al. 2016

Fish Sci

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“…For larger n freq values it is hard to see any difference between the methods. Though there are small differences between the 1FC approaches, the linear transformation of y-values give better results than the log-transformation, and the goodness of fit values are best for NCC for the n freq values (2)(3)(4)(5) where the difference between NCC and NEAC is largest.…”

Section: Goodness Of Fitmentioning

confidence: 97%

“…A recipe for transforming a 2D contour to 1D is given in [5], which we denote MIRR because it mirrors the lower part of the contour around a vertical axis at the maximum x-value of the contour. The method is described in more detail in the method section.…”

Section: Introductionmentioning

confidence: 99%

Perfect representation of a convex 2D contour with only one Fourier component

Harbitz

2020

Preprint

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Shape analysis of a closed 2D contour is an important topic within biological shape analysis, where Fourier methods to reproduce the shape with a limited number of parameters have been and still are of vital importance. An example is within marine management research on fish, where shape analysis of otolith (earstone) contours is performed for species identification as well as for stock discrimination purposes. In both cases, it is expected that the fewer parameters that are needed in a method to reproduce the contour sufficiently good, the better. This contribution outlines how a convex contour of any shape can be represented to any wanted accuracy by only one Fourier component. The key idea is to allow a flexible choice of a predetermined number of x-values along an x-axis that goes through the two most distant points of the contour. The y-variable along the perpendicular y-axis is then monotonically transformed to a z-variable so that the minium and maximum z-values on the contour have the same distance from the x-axis. The x-values of the contour points are now chosen so that the corresponding z-values on the contour follows a perfect sinusoid if the x-values were equidistant. The method is illustrated by application to lasso contours of Norwegian Coastal Cod (NCC) and North East Arctic Cod (NEAC) otolith images, where the average new x-positions for the individual otolith contours were applied to all otoliths. The results show that a considerably better fit to the original individual otolith contours were obtained by applying the invers FFT to the new y-values than by the frequently applied 2D EFDs (Elliptical Fourier Descriptors) approach, for the same number, m < 11, of frequency components. A promising classification result was also obtained by the linear Fisher discrimination method and cross validation applied to the individual x-values for the NCC and NEAC otoliths, with 82% score for NCC and 80% score for NEAC with sample sizes 367 and 240, respectively.

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“…Contributions represented in this issue include the development of a novel statistical methodology for shape analysis (Reig-Bolano et al 2010) along with a direct comparison of otolith chemistry and shape analyses to examine spatial structure in a deep-sea species (Longmore et al 2010). There is also an examination of otolith ecomorphological trends in relation to the phylogeny of Nototheniid fishes .…”

Section: Physiology and Morphologymentioning

confidence: 99%