Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at https://github.com/BehnoodRasti/HySUPP to reproduce the results

Evolution of red blood cell membrane complement regulatory proteins and rheology in septic patients: An exploratory study

BackgroundDuring sepsis, red blood cell (RBC) deformability is altered. Persistence of these alterations is associated with poor outcome. Activation of the complement system is enhanced during sepsis and RBCs are protected by membrane surface proteins like CD35, CD55 and CD59. In malaria characterized by severe anemia, a study reported links between the modifications of the expression of these RBCs membrane proteins and erythrophagocytosis. We studied the evolution of RBCs deformability and the expression of RBC membrane surface IgG and regulatory proteins in septic patients.MethodsBy flow cytometry technics, we measured at ICU admission and at day 3–5, the RBC membrane expression of IgG and complement proteins (CD35, 55, 59) in septic patients compared to RBCs from healthy volunteers. Results were expressed in percentage of RBCs positive for the protein. RBC shape was assessed using Pearson's second coefficient of dissymmetry (PCD) on the histogram obtained with a flow cytometer technique. A null value represents a perfect spherical shape. RBC deformability was determined using ektacytometry by the elongation index in relation to the shear stress (0.3–50 Pa) applied to the RBC membrane. A higher elongation index indicates greater RBC deformability.ResultsRBCs from 11 septic patients were compared to RBCs from 21 volunteers. At ICU admission, RBCs from septic patients were significantly more spherical and RBC deformability was significantly lower in septic patients for all shear stress ≥1.93 Pa. These alterations of shape and deformability persists at day 3–5. We observed a significant decrease at ICU admission only in CD35 expression on RBCs from septic patients. This low expression remained at day 3–5.ConclusionsWe observed in RBCs from septic patients a rapid decrease expression of CD35 membrane protein protecting against complement activation. These modifications associated with altered RBC deformability and shape could facilitate erythrophagocytosis, contributing to anemia observed in sepsis. Other studies with a large number of patients and assessment of erythrophagocytosis were needed to confirm these preliminary data

Personalized pathology test for Cardio-vascular disease : approximate Bayesian computation with discriminative summary statistics learning

Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment

A simplified mesoscale 3D model for characterizing fibrinolysis under flow conditions

One of the routine clinical treatments to eliminate ischemic stroke thrombi is injecting a biochemical product into the patient’s bloodstream, which breaks down the thrombi’s fibrin fibers: intravenous or intravascular thrombolysis. However, this procedure is not without risk for the patient; the worst circumstances can cause a brain hemorrhage or embolism that can be fatal. Improvement in patient management drastically reduced these risks, and patients who benefited from thrombolysis soon after the onset of the stroke have a significantly better 3-month prognosis, but treatment success is highly variable. The causes of this variability remain unclear, and it is likely that some fundamental aspects still require thorough investigations. For that reason, we conducted in vitro flow-driven fibrinolysis experiments to study pure fibrin thrombi breakdown in controlled conditions and observed that the lysis front evolved non-linearly in time. To understand these results, we developed an analytical 1D lysis model in which the thrombus is considered a porous medium. The lytic cascade is reduced to a second-order reaction involving fibrin and a surrogate pro-fibrinolytic agent. The model was able to reproduce the observed lysis evolution under the assumptions of constant fluid velocity and lysis occurring only at the front. For adding complexity, such as clot heterogeneity or complex flow conditions, we propose a 3-dimensional mesoscopic numerical model of blood flow and fibrinolysis, which validates the analytical model’s results. Such a numerical model could help us better understand the spatial evolution of the thrombi breakdown, extract the most relevant physiological parameters to lysis efficiency, and possibly explain the failure of the clinical treatment. These findings suggest that even though real-world fibrinolysis is a complex biological process, a simplified model can recover the main features of lysis evolution.</p

Effect of Form Factor and Mass Fraction of Alfa Short Fibers on the Mechanical Behavior of an Alfa/Greenpoxy Bio-composite

International audienceAbstract: This experimental study highlights the effect of the geometric form factor (λ=length/diameter) and the fiber mass fraction on the mechanical behavior of an Alfa/Greenpoxy bio-composite. The Alfa stalks, collected in the Djelfa region in the Algeria highlands, were cut into pieces with length of 7 to 10 cm, washed and dried for two days at 70 °C. Using a knife mill coupled to three sieves (1.6, 2 or 2.5 mm), three categories of short fibers, according to their form factor λ, were obtained. Depending on the incorporated mass fraction (5, 10, 15 or 20 %) and the three form factors λ of the fibers, twelve types of plates were manufactured by hand molding followed by a curing cycle to accelerate the polymerization, reduce porosity and improve the final surface state. The main mechanical characteristics were determined with tensile, bending and shock tests on ISO 3167-type A samples, obtained by laser cutting. The results revealed that the increase of the form factor and the mass fraction gives rise to a significant improvement of the mechanical properties. We conclude that optimal processing conditions will maximize the mechanical properties of Alfa/Greenpoxy bio-composites.RÉSUMÉ :Cette étude expérimentale met en valeur l’effet du facteur de forme géométrique (λ=longueur/diamètre) et de la fraction massique de fibre sur le comportement mécanique d’un bio-composite Alfa/Greenpoxy. Les tiges d’Alfa, collectées dans la région de Djelfa des hauts plateaux de l’Algérie, sont découpées en morceaux de 7 à 10 cm, lavées et séchées durant deux jours à 70 °C. Trois catégories de fibres courtes, suivant leur facteur de forme λ, ont été obtenues par passage dans un broyeur à couteaux suivi d'un tamisage (tamis 1,6, 2 ou 2,5 mm). En fonction de la fraction massique incorporée (5, 10, 15 ou 20 %) et des trois facteurs de forme λ des fibres, douze types de plaques ont été élaborés par moulage manuel suivi d’un cycle de cuisson pour accélérer la polymérisation, réduire la porosité et améliorer l’état de surface final. Des échantillons ISO 3167-type A, prélevés par découpe laser, ont été testés en traction, flexion et choc pour déterminer les principales caractéristiques mécaniques. Les résultats ont révélé que l’augmentation du facteur de forme et de la fraction massique engendre une sensible amélioration des propriétés mécaniques. Nous concluons que l’adoption de conditions optimales permettra de maximiser les propriétés mécaniques des bio-composites Alfa/Greenpoxy

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

International audienceHyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics. Unfortunately, the spectral diversity of information comes at the expense of various sources of degradation, and the lack of accurate ground-truth "clean" hyperspectral signals acquired on the spot makes restoration tasks challenging. In particular, training deep neural networks for restoration is difficult, in contrast to traditional RGB imaging problems where deep models tend to shine. In this paper, we advocate instead for a hybrid approach based on sparse coding principles that retains the interpretability of classical techniques encoding domain knowledge with handcrafted image priors, while allowing to train model parameters end-to-end without massive amounts of data. We show on various denoising benchmarks that our method is computationally efficient and significantly outperforms the state of the art

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at https://github.com/BehnoodRasti/HySUPP to reproduce the results

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

International audienceHyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics. Unfortunately, the spectral diversity of information comes at the expense of various sources of degradation, and the lack of accurate ground-truth "clean" hyperspectral signals acquired on the spot makes restoration tasks challenging. In particular, training deep neural networks for restoration is difficult, in contrast to traditional RGB imaging problems where deep models tend to shine. In this paper, we advocate instead for a hybrid approach based on sparse coding principles that retains the interpretability of classical techniques encoding domain knowledge with handcrafted image priors, while allowing to train model parameters end-to-end without massive amounts of data. We show on various denoising benchmarks that our method is computationally efficient and significantly outperforms the state of the art