Semi-local extraction of ring structures in images of biological hard tissues: application to the Bayesian interpretation of fish otoliths for age and growth estimation

Abstract

This paper deals with the analysis of images of biological tissue that involves ring structures, such as tree trunks, bivalve seashells, or fish otoliths, with a view to automating the acquisition of age and growth data. A bottom-up template-based scheme extracts meaningful ridge and valley curve data using growth-adapted time-frequency filtering. Age and growth estimation is then stated as the Bayesian selection of a subset of ring curves, which combines a measure of curve significativity and an a priori statistical growth model. Experiments on real samples demonstrate the efficiency of the proposed data extraction stage. Our Bayesian framework is shown to significantly outperform previous methods for the interpretation of a data set of 200 plaice otoliths and compares favorably with interexpert agreement rates (88% of agreement to expert interpretations). Problem statement and related workAge and growth data provide key information for a broad range of scientific issues: for instance, the computation of daily temperature series for paleoclimatology from the analysis of daily increments 30 on the shell of sea scallops (Chauvaud 2004), the determination of age-length keys from the interpretation of fish otoliths for marine stock assessments (Panfili et al. 2003), the analysis of fish otolith growth patterns for marine ecology (Hare and Cowen 1997;Panfili et al. 2003) or tree-ring dating for archeology (Baillie 1982). All of these applications initially rely on the interpretation of growth rings observed on biological materials such as tree trunks, corals, shells of seashells, fish otoliths, fish scales, 35 which will be subsequently referred as biological hard tissues (Fig. 1). The presence of concentric ring structures is due to the variations in chemistry during the accretionary formation of these structures.The deposit of the rings is often associated with a biological periodicity (either seasonal or daily, depending on the species and the tissue). Counting growth rings then leads to an estimation of the age of the analyzed tissue, whereas growth increments can be estimated from the distance between successive 40 rings (Fig. 2), however, not all the observed ring structures are relevant for aging (Campana 2001).The previously mentioned biological applications require large collections of age and growth data for a consistent analysis. The acquisition of this data is generally performed by expert readers (typically, for marine stock assessment issues, several thousands of fish otoliths per year). The development of digital imaging systems and computer-aided tools brings new solutions to this field, (Small and 45 Hirschhorn 1987;Guay 1997;Morison et al. 1998). Our work focuses on the automation of age and growth estimation from the analysis of ring structures within the images of biological hard tissues. As detailed below, the key issues lie in the use of growth information to design a ring extraction framework that takes into account growth non-linearity (Fig. 2.b), and to discriminate actual growth rings...

Age estimation of billfishes (Kajikiaspp.) using fin spine cross-sections: the need for an international code of practice

“…A complete annulus consists of wide opaque zone (fast growth) followed by a narrow translucent zone (slow growth) when viewed under reflected light (Berkley and Houde 1983;Fablet 2006). The appearance of growth zones is reversed when sections are viewed under transmitted light.…”

“…Annuli presumed to form on a yearly basis are continuous around the circumference of fin spine sections (Berkley and Houde 1983) and do not show breaks or partially formed checks (DeMartini et al 2007). In practice, however, it is difficult to apply general criteria (see Fablet 2006) to derive "age" estimates from annulus counts. Due to the interpretative skills required to derive accurate age estimates from calcified hard-parts, the process of counting annuli is generally referred to as reading.…”

Reconstructing individual shape histories of fish otoliths: A new image-based tool for otolith growth analysis and modeling

“…Following ongoing developments (Alvarez et al, 2008;Fablet, 2006;Fablet et al, 2007) aimed at information extraction and interpretation in fish otolith images, this paper addresses the extraction of geometric otolith characteristics and their application to otolith growth modelling and analysis. Though extensively studied and exploited (Campana and Casselman, 1993;de Pontual and Prouzet, 1988), the analysis of the shape of fish otoliths and other calcified structures has usually been restricted to the analysis of the outline of the otolith in a given observation plane, especially for stock and species discrimination.…”

2D Image-based reconstruction of shape deformation of biological structures using a level-set representation

“…New computational tools [18,19] are sought to improve the understanding, the modeling and the decoding of these biological archives. Their shape characteristics have been extensively exploited [5].…”

“…The application of these schemes would however first require extracting the internal growth rings. Whereas the detection of the growth center and the detection of the external shape may be automated [6], the automated extraction of internal growth rings is a particularly complex task due to the presence of blind areas and so-called subjective contours [18,21]. As exemplified in Fig.…”