Path openings and closings are morphological tools used to preserve long, thin, and tortuous structures in gray level images. They explore all paths from a defined class, and filter them with a length criterion. However, most paths are redundant, making the process generally slow. Parsimonious path openings and closings are introduced in this paper to solve this problem. These operators only consider a subset of the paths considered by classical path openings, thus achieving a substantial speed-up, while obtaining similar results. In addition, a recently introduced 1D opening algorithm is applied along each selected path. Its complexity is linear with respect to the number of pixels, independent of the size of the opening. Furthermore, it is fast for any input data accuracy (integer or floating point) and works in stream. Parsimonious path openings are also extended to incomplete paths, i.e., paths containing gaps. Noise-corrupted paths can thus be processed with the same approach and complexity. These parsimonious operators achieve a several orders of magnitude speed-up. Examples are shown for incomplete path openings, where computing times are brought from minutes to tens of milliseconds, while obtaining similar results.

SummaryBoehmite occurs in the form of nanoparticles. Upon drying, it can form the alumina that is common in catalyst support used in refining and petrochemicals. The topotactic transformation of boehmite alumina led to an interest in the precise shape and size of these nanoparticles which is highly linked to the catalyst activity. Boehmite nanoparticles can be observed by transmission electron microscopy. Although they are highly aggregated, the analysis of transmission electron microscopy images with a specific random model approach, here a dilution model, can give an accurate estimate of their size. To use this approach, electronic noise and diffraction artefacts on the edges of the nanoparticles have to be removed. Covariance measurements on micrographs can be performed. They can be used to fit a model. The fitting uses a novel numerical method to estimate the covariogram of grains. The model can take into account the specific orientations of the nanoparticles. The influence of noise, image filters used to remove noise and diffraction artefacts, as well as all the parameters of the model are all studied in this paper. We propose nanoparticle size estimations procedures based on both single and mixture-oftwo particle models.

International audienceMultiphoton microscopy has emerged in the past decade as a promising non-invasive skin imaging technique. The aim of this study was to assess whether multiphoton microscopy coupled to specific 3D image processing tools could provide new insights into the organization of different skin components and their age-related changes. For that purpose, we performed a clinical trial on 15 young and 15 aged human female volunteers on the ventral and dorsal side of the forearm using the DermaInspectR medical imaging device. We visualized the skin by taking advantage of intrinsic multiphoton signals from cells, elastic and collagen fibers. We also developed 3D image processing algorithms adapted to in vivo multiphoton images of human skin in order to extract quantitative parameters in each layer of the skin (epidermis and superficial dermis). The results show that in vivo multiphoton microscopy is able to evidence several skin alterations due to skin aging: morphological changes in the epidermis and modifications in the quantity and organization of the collagen and elastic fibers network. In conclusion, the association of multiphoton microscopy with specific image processing allows the three-dimensional organization of skin components to be visualized and quantified thus providing a powerful tool for cosmetic and dermatological investigations

We introduce a new, efficient and adaptable algorithm to compute openings, granulometries and the component tree for onedimensional (1-D) signals. The algorithm requires only one scan of the signal, runs in place in O(1) per pixel, and supports any scalar data precision (integer or floating-point data). The algorithm is applied to two-dimensional images along straight lines, in arbitrary orientations. Oriented size distributions can thus be efficiently computed, and textures characterised. Extensive benchmarks are reported. They show that the proposed algorithm allows computing 1-D openings faster than existing algorithms for data precisions higher than 8 bits, and remains competitive with respect to the algorithm proposed by Van Droogenbroeck when dealing with 8-bit images. When computing granulometries, the new algorithm runs faster than any other method of the state of the art. Moreover, it allows efficient computation of 1-D component trees.

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