Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201

Psoriasis and mental health workshop report : Exploring the links between psychosocial factors, psoriasis, neuroinflammation and cardiovascular disease risk

Psoriasis is a systemic, relapsing, inflammatory disease associated with serious comorbidities including mood problems and/or unhealthy lifestyle behaviours. Cutaneous and systemic abnormalities in innate and acquired immunity play a role in its pathogenesis. The exact pathogenetic mechanism remains elusive. Evidence is accumulating that TNF-alpha, IL-17 and IL-23 signalling are highly relevant as targeting these pathways reduces disease activity. Evidence suggests a strong link between psoriasis and depression in adults. The International Psoriasis Council (IPC) held a roundtable event, "Psoriasis and Mental Health", in Barcelona, Spain which focused on the presence of depression and suicidality, plus the role of neuroinflammation in psoriasis, sleep disruption and the impact of depression on cardiovascular disease outcomes. We summarize here the expert presentations to provide additional insight into the understanding of psychiatric comorbidities of psoriasis and of the impact of chronic, systemic inflammation on neuro-and cardiovascular outcomes. the associations between psoriasis and other psychiatric comorbidities are still controversial and warrant further attention

Supported oligomethionine sulfoxide and Ellman's reagent for cysteine bridges formation

cited By 0International audienceA large number of bioactive peptides are cyclized through a disulfide bridge. This structural feature is very important for both bioactivity and stability. The oxidation of cysteine side chains is challenging not only to avoid intermolecular reaction leading to oligomers and oxidation of other residues but also to remove solvents and oxidant such as dimethyl sulfoxide. Supported reagents advantageously simplify the work-up of such disulfide bond formation, but may lead to a significant decrease in yield of the oxidized product. In this study, two resins working through different mechanisms were evaluated: Clear-Ox, a supported version of Ellman's reagent and Oxyfold, consisting in a series of oxidized methionine residues. The choice of the supported reagent is discussed on the light of reaction speed, side-products formation and yield considerations. © 2012 Springer-Verlag

Oxyfold: A simple and efficient solid-supported reagent for disulfide bond formation

cited By 3International audienceThe synthesis and use of novel polymer-supported reagents for disulfide bond formation is described. This family of supported reagents consists of a series of oxidized methionines grafted onto a solid support. Their cost and the simplicity of their preparation through N-carboxyanhydride polymerization on beads make them reactants of choice for the formation of disulfide bridges in peptides. Copyright © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Biophysical characterization of a binding site for TLQP-21, a naturally occurring peptide which induces resistance to obesity.

Recently, we demonstrated that TLQP-21 triggers lipolysis and induces resistance to obesity by reducing fat accumulation [1]. TLQP-21 is a 21 amino acid peptide cleavage product of the neuroprotein VGF and was first identified in rat brain. Although TLQP-21 biological activity and its molecular signaling is under active investigation, a receptor for TLQP-21 has not yet been characterized. We now demonstrate that TLQP-21 stimulates intracellular calcium mobilization in CHO cells. Furthermore, using Atomic Force Microscopy (AFM), we also provide evidence of TLQP-21 binding-site characteristics in CHO cells. AFM was used in force mapping mode equipped with a cantilever suitably functionalized with TLQP-21. Attraction of this functionalized probe to the cell surface was specific and consistent with the biological activity of TLQP-21; by contrast, there was no attraction of a probe functionalized with biologically inactive analogues. We detected interaction of the peptide with the binding-site by scanning the cell surface with the cantilever tip. The attractive force between TLQP-21 and its binding site was measured, statistically analyzed and quantified at approximately 40 pN on average, indicating a single class of binding sites. Furthermore we observed that the distribution of these binding sites on the surface was relatively uniform

Marked Point Process Model for Curvilinear Structures Extraction

International audienceIn this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets

Brain-inspired robust delineation operator

In this paper we present a novel filter, based on the existing COSFIRE filter, for the delineation of patterns of interest. It includes a mechanism of push-pull inhibition that improves robustness to noise in terms of spurious texture. Push-pull inhibition is a phenomenon that is observed in neurons in area V1 of the visual cortex, which suppresses the response of certain simple cells for stimuli of preferred orientation but of non-preferred contrast. This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture. We performed experiments on images from different applications, namely the detection of rose stems for automatic gardening, the delineation of cracks in pavements and road surfaces, and the segmentation of blood vessels in retinal images. Push-pull inhibition helped to improve results considerably in all applications.Comment: Accepted at Brain-driven Computer Vision workshop at ECCV 201

Detecting parametric objects in large scenes by Monte Carlo sampling

International audiencePoint processes constitute a natural extension of Markov Random Fields (MRF), designed to handle parametric objects. They have shown efficiency and competitiveness for tackling object extraction problems in vision. Simulating these stochastic models is however a difficult task. The performances of the existing samplers are limited in terms of computation time and convergence stability, especially on large scenes. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits the Markovian property of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism so that the points are distributed in the scene in function of spatial information extracted from the input data. The performances of the sampler are analyzed through a set of experiments on various object detection problems from large scenes, including comparisons to the existing algorithms. The sampler is also tested as optimization algorithm for MRF-based labeling problems