Many approaches for image segmentation rely on a first low-level segmentation step, where an image is partitioned into homogeneous regions with enforced regularity and adherence to object boundaries. Methods to generate these superpixels have gained substantial interest in the last few years, but only a few have made it into applications in practice, in particular because the requirements on the processing time are essential but are not met by most of them. Here, we propose waterpixels as a general strategy for generating superpixels which relies on the marker controlled watershed transformation. We introduce a spatially regularized gradient to achieve a tunable tradeoff between the superpixel regularity and the adherence to object boundaries. The complexity of the resulting methods is linear with respect to the number of image pixels. We quantitatively evaluate our approach on the Berkeley segmentation database and compare it against the state-of-the-art.

International audienceMany sophisticated segmentation algorithms rely on a first low-level segmentation step where an image is partitioned into homogeneous regions with enforced compactness and adherence to object boundaries. These regions are called " su-perpixels ". While the marker controlled watershed transformation should in principle be well suited for this type of application , it has never been seriously tested in this setup, and comparisons to other methods were not made with the best possible settings. Here, we provide a scheme for applying the watershed transform for superpixel generation, where we use a spatially regularized gradient to achieve a tunable trade-off between superpixel regularity and adherence to object boundaries. We quantitatively evaluate our method on the Berkeley segmenta-tion database and show that we achieve comparable results to a previously published state-of-the art algorithm, while avoiding some of the arbitrary postprocessing steps the latter requires

The assembly of proteins into fibrillar structures is an important process that concerns different biological contexts, including molecular medicine and functional biomaterials. Engineering of hybrid biomaterials can advantageously provide synergetic interactions of the biopolymers with an inorganic component to ensure specific supramolecular organization and dynamics. To this aim, we designed hybrid systems associating collagen and surface-functionalized silica particles and we built a new strategy to investigate fibrillogenesis processes in such multicomponents systems, working at the crossroads of chemistry, physics and mathematics. The self-assembly process was investigated by bimodal multiphoton imaging coupling second harmonic generation (SHG) and 2 photon excited fluorescence (2PEF). The in-depth spatial characterization of the system was further achieved using the three-dimensional analysis of the SHG/2PEF data via mathematical morphology processing. Quantitation of collagen distribution around particles offers strong evidence that the chemically induced confinement of the protein on the silica nanosurfaces has a key influence on the spatial extension of fibrillogenesis. This new approach is unique in the information it can provide on 3D dynamic hybrid systems and may be extended to other associations of fibrillar molecules with optically responsive nano-objects.

Segmenting an image is usually one of the major and most challenging steps in the pipeline of biomedical image analysis. One classical and promising approach is to consider segmentation as a classification task, where the aim is to assign to each pixel the label of the objects it belongs to. Pixels are therefore described by a vector of features, where each feature is calculated on the pixel itself or, more frequently, on a sliding window centered on the pixel. In this work, we propose to replace the sliding window by superpixels, i.e. regions which adapt to the image content. We call the resulting features SAF (Superpixel Adaptive Feature). Their contribution is highlighted on a biomedical database of melanocytes images. Qualitative and quantitative analyses show that they are better suited for segmentation purposes than the sliding window approach.