Newly-developed parameters to quantify texture : application to polymeric porous structures

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Multiscale photoacoustic tomography using reversibly switchable bacterial phytochrome as a near-infrared photochromic probe.

Photoacoustic tomography (PAT) of genetically encoded probes allows for imaging of targeted biological processes deep in tissues with high spatial resolution; however, high background signals from blood can limit the achievable detection sensitivity. Here we describe a reversibly switchable nonfluorescent bacterial phytochrome for use in multiscale photoacoustic imaging, BphP1, with the most red-shifted absorption among genetically encoded probes. BphP1 binds a heme-derived biliverdin chromophore and is reversibly photoconvertible between red and near-infrared light-absorption states. We combined single-wavelength PAT with efficient BphP1 photoswitching, which enabled differential imaging with substantially decreased background signals, enhanced detection sensitivity, increased penetration depth and improved spatial resolution. We monitored tumor growth and metastasis with ∼ 100-μm resolution at depths approaching 10 mm using photoacoustic computed tomography, and we imaged individual cancer cells with a suboptical-diffraction resolution of ∼ 140 nm using photoacoustic microscopy. This technology is promising for biomedical studies at several scales.

Newly-developed parameters to quantify texture : application to polymeric porous structures

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy

This paper addresses the problem of colorectal tumour segmentation in complex real world imagery. For efficient segmentation, a multi-scale strategy is developed for extracting the potentially cancerous region of interest (ROI) based on colour histograms while searching for the best texture resolution. To achieve better segmentation accuracy, we apply a novel bag-of-visual-words method based on rotation invariant raw statistical features and random projection based l2-norm sparse representation to classify tumour areas in histopathology images. Experimental results on 20 real world digital slides demonstrate that the proposed algorithm results in better recognition accuracy than several state of the art segmentation techniques.

Multiscale Analysis for Field-Effect Penetration through Two-Dimensional Materials

Gate-tunable two-dimensional (2D) materials-based quantum capacitors (QCs) and van der Waals heterostructures involve tuning transport or optoelectronic characteristics by the field effect. Recent studies have attributed the observed gate-tunable characteristics to the change of the Fermi level in the first 2D layer adjacent to the dielectrics, whereas the penetration of the field effect through the one-molecule-thick material is often ignored or oversimplified. Here, we present a multiscale theoretical approach that combines first-principles electronic structure calculations and the Poisson–Boltzmann equation methods to model penetration of the field effect through graphene in a metal–oxide–graphene–semiconductor (MOGS) QC, including quantifying the degree of “transparency” for graphene two-dimensional electron gas (2DEG) to an electric displacement field. We find that the space charge density in the semiconductor layer can be modulated by gating in a nonlinear manner, forming an accumulation or inversion layer at the semiconductor/graphene interface. The degree of transparency is determined by the combined effect of graphene quantum capacitance and the semiconductor capacitance, which allows us to predict the ranking for a variety of monolayer 2D materials according to their transparency to an electric displacement field as follows: graphene > silicene > germanene > WS2 > WTe2 > WSe2 > MoS2 > phosphorene > MoSe2 > MoTe2, when the majority carrier is electron. Our findings reveal a general picture of operation modes and design rules for the 2D-materials-based QCs.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.

A Note on Iterations-based Derivations of High-order Homogenization Correctors for Multiscale Semi-linear Elliptic Equations

This Note aims at presenting a simple and efficient procedure to derive the structure of high-order corrector estimates for the homogenization limit applied to a semi-linear elliptic equation posed in perforated domains. Our working technique relies on monotone iterations combined with formal two-scale homogenization asymptotics. It can be adapted to handle more complex scenarios including for instance nonlinearities posed at the boundary of perforations and the vectorial case, when the model equations are coupled only through the nonlinear production terms.

Effet de réduction en sodium sur la texture et la bioaccessibilité des protéines d’un fromage à pâte molle à croûte fleurie

Le sel (NaCl) joue plusieurs rôles importants dans les fromages aux niveaux technologique, microbiologique et organoleptique. Cependant, le sodium, un facteur de risque des maladies cardiovasculaires, est consommé en trop grande quantité par les Canadiens. Comme la réduction du sodium pourrait affecter la texture des fromages à pâte molle à croûte fleurie et la libération des nutriments pendant la digestion, l’impact de la réduction du sodium sur la bioaccessibilité des protéines dans le fromage Brie a été étudié. Ainsi, la composition et les caractéristiques texturales de fromages Brie ayant différentes teneurs en sodium (standard, réduite, substituée au KCl) ont été analysées. Une réduction du sodium de 23 % a été obtenue pour les fromages réduits en sodium. Le temps d’affinage affecte la protéolyse et la texture des fromages. La cinétique de dégradation de la matrice fromagère et la libération des protéines ont été déterminées par une approche de digestion in vitro. La désintégration de la matrice fromagère et la libération des protéines pendant la digestion in vitro ne sont pas différentes entre les fromages expérimentaux. Ces travaux démontrent qu’il est possible de réduire le sodium sans affecter le profil de protéolyse et la texture pendant l’affinage du fromage Brie ainsi que son comportement à la digestion.

Multiscale morphology design of hybrid halide perovskites through a polymeric template

Hybrid halide perovskites have emerged as promising active constituents of next generation solution processable optoelectronic devices. During their assembling process, perovskite components undergo very complex dynamic equilibria starting in solution and progressing throughout film formation. Finding a methodology to control and affect these equilibria, responsible for the unique morphological diversity observed in perovskite films, constitutes a fundamental step towards a reproducible material processability. Here we propose the exploitation of polymer matrices as cooperative assembling components of novel perovskite CH3NH3PbI3 : polymer composites, in which the control of the chemical interactions in solution allows a predictable tuning of the final film morphology. We reveal that the nature of the interactions between perovskite precursors and polymer functional groups, probed by Nuclear Magnetic Resonance (NMR) spectroscopy and Dynamic Light Scattering (DLS) techniques, allows the control of aggregates in solution whose characteristics are strictly maintained in the solid film, and permits the formation of nanostructures that are inaccessible to conventional perovskite depositions. These results demonstrate how the fundamental chemistry of perovskite precursors in solution has a paramount influence on controlling and monitoring the final morphology of CH3NH3PbI3 (MAPbI3) thin films, foreseeing the possibility of designing perovskite : polymer composites targeting diverse optoelectronic applications.