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A region-based level-set active contour is the preferred choice for image-segmentation tasks, when the region of interest is defined by weak edges. While, the minimization of an energy functional leads to the contour’s conformation to object boundary, the energy is composed of contour’s internal and image-dependent external energies. In such a model, the overall motion (expansion/shrinkage) of contour is controlled by its area-energy, whereas the length-energy controls the contour’s elasticity. Traditionally, these two internal energies are weighted by scalar constants, which remain fixed throughout the level-set evolution. Both the internal energies are responsible for the contour’s regularization only, whereas the contour converges due to the minimization of external energies. Further, inappropriate weighting results into an inaccurate segmentation either because of premature convergence, or leakage beyond the object boundary. To address these issues, an adaptive spatially weighted region-based active contour (ASWRAC) is proposed. The weights are assigned adaptively to the internal energy terms at successive steps of evolution. This basically removes the need of weight initialization for an improved segmentation process. Additionally, the proposed weights are modeled as vectors, depending on the local regional statistics of the image. This causes the originally internal energies to also reflect external characteristics. Moreover, the time-step used in the discrete implementation of level-set evolution is also managed adaptively. This restricts the contour’s leakage beyond the region of interest. The suggested technique is tested on brain MR slices and other biomedical images from public databases. Comparison of the obtained results with state-of-the-art techniques shows its superiority in segmentation accuracy measured using the metrics: dice Similarity Coefficient, sensitivity, and specificity.

Crack initiation and propagation analysis in brittle two-dimensional isotropic materials are conducted using the damage Phase Field Method (PFM) following the variational approach. Here, we study two-dimensional (2D) structures that contain material heterogeneities (e.g., interfaces and inclusions) as well as geometric ones (e.g., cracks and voids) when subjected to quasi-static uniaxial tensile loading. The adopted methodology combines Extended Finite Element Method (XFEM) and Level-Set couple with PFM in order to investigate both the case of heterogeneous materials (composite or porous) as well as the case of interfacial cracks between two different materials for their interest and relevance as practical examples. Among many others, one can, for example, argue that: (a) the regular hexagonal arrangement of heterogeneities leads to a significantly higher strength than any random arrangement, for both the composite material (containing fibers) and the porous material (containing voids), (b) the effects of contrasting stiffness and toughness between two materials along with their respective impacts on the interfacial crack trajectory, on energy balance and on reaction force are in favor of toughness.

This study aims to estimate elastic properties as well as voids interaction energy within the context of 3D nano-porous materials exhibiting spherical or cylindrical voids, with or without the effect of surface energy. For this purpose, numerical homogenization is applied, based on the finite element method (FEM) in combination with the level-set technique. Surface energy is approximated by explicitly taking into account its contribution to overall stiffness in the variational formulation. Several cases are then investigated to appreciate the interaction energy between multiple 3D nanovoids on the actual behavior of nanomaterials. As expected, and like surface energy, the results show that interaction energy keeps affecting the effective behavior by increasing the size of RVE as well as the number of voids inside. This influence is more marked the more cylindrical the voids and the more closely they are adjacent to each other.

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.