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

Abstract

This paper copes with the reconstruction of accretionary growth sequence from images of biological structures depicting concentric ring patterns. Accretionary growth shapes are modeled as the level-sets of a potential function. Given an image of a biological structure, the reconstruction of the sequence of growth shapes is stated as a variational issue derived from geometric criteria. This variational setting exploits image-based information, in terms of the orientation field of relevant image structures, which leads to an original advection term. The resolution of this variational issue is discussed. Experiments on synthetic and real data are reported to validate the proposed approach.

Shedding Light on Fish Otolith Biomineralization Using a Bioenergetic Approach

“…Otolith opacity O relates to variations in the ratio between the volumes ΔV P and ΔV C of the organic and mineral fractions of the newly precipitated material [19] : The temporal simulation of otolith formation is transformed into a 2D transverse section image of an otolith using calibrated shape deformation algorithms [20] . This allows comparing simulated otolith images to real ones.…”

“…3) can be regarded as functions of the state of the individual (reserves and length) and of its environment (temperature and food density). The 1D simulation of otolith formation may be transformed into a 2D transverse section image of a growing otolith using calibrated shape deformation algorithms for otolith images [20] .…”

Shape geodesics for the classification of calcified structures: Beyond Fourier shape descriptors

“…From a methodological point of view, two aspects will be further explored: the determination of statistical shape models (Charpiat et al, 2005;Klassen et al, 2004) and the combination to kernel-based statistical learning (Ben-Hur et al, 2008;Fablet and Josse, 2005). Besides, we have recently proposed a technique for reconstructing individual shape histories (Fablet et al, 2009(Fablet et al, , 2008. The combination of such shape histories to shape geodesics will be considered to achieve classification not only from the external CS shape, but from entire shape sequence.…”

Non-rigid registration of shape sequence images: Applications to biological and medical images

“…In a previous work [3], we have developped a technique for the estimation of a level-set function that represents the shape sequence of the original image by a variational level-set representation ( Fig. 1).…”