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Microscopic imaging in the biomedical sciences allows for detailed study of the structure and function of normal and abnormal (i.e., diseased) states of cells and tissues. The expression patterns of proteins and/or physiological parameters within these specimens can be related to disease progression and prognosis, and are often heterogeneously spread throughout the entire specimen. With conventional microscopy, a large number of individual image ‘tiles’ must be captured and subsequently combined into a mosaic of the entire specimen. This has the potential to introduce artefacts at the image seams, as well as introducing non-uniform illumination of the entire specimen. A further limitation often encountered in biomedical fluorescence microscopy is the high background due to the autofluorescence (AF) of endogenous compounds within cells and tissues. Often, AF can prevent the detection and/or accurate quantification in fluorescently- labelled tissues and, in general, can reduce the reliability of results obtained from such specimens. AF spectra are relatively broad and so can be present across a large number of image spectral channels. The intensity of AF also increases as the excitation wavelength is decreased, causing increasing amounts of autofluorescence when exciting in the blue and near-UV range of the spectrum (400 - 500 nm). This thesis reports the development of hyperspectral, fluorescence and brightfield imaging of entire, paraffin-embedded, formalin-fixed (PEFF) tissue slides using a prototype confocal scanner with a large field of view (FOV). This technology addresses the challenges of imaging large tissue sections through the use of a telecentric f-theta laser scan lens thus allowing an entire microscope slide (22x70 mm) to be imaged in a single scan at resolution equivalent to a 10x microscope objective. The development and optimization of brightfield and single-channel fluorescence imaging modes are discussed in the first half of this thesis, while the second half and appendices concentrate on the spectral properties of the system and removal of AF from PEFF tissue sections. The hyperspectral imaging mode designed for this system allows the fluorescence emission spectrum of each image pixel to be sampled at 6.7 nm/channel over a spectral range of 500-700 nm. This results in the ability to separate distinct fluorescence signatures from each other, and enables quantification even in situations where the AF completely masks the signal from the applied labels.

Microscopic imaging in the biomedical sciences allows for detailed study of the structure and function of normal and abnormal (i.e., diseased) states of cells and tissues. The expression patterns of proteins and/or physiological parameters within these specimens can be related to disease progression and prognosis, and are often heterogeneously spread throughout the entire specimen. With conventional microscopy, a large number of individual image ‘tiles’ must be captured and subsequently combined into a mosaic of the entire specimen. This has the potential to introduce artefacts at the image seams, as well as introducing non-uniform illumination of the entire specimen. A further limitation often encountered in biomedical fluorescence microscopy is the high background due to the autofluorescence (AF) of endogenous compounds within cells and tissues. Often, AF can prevent the detection and/or accurate quantification in fluorescently- labelled tissues and, in general, can reduce the reliability of results obtained from such specimens. AF spectra are relatively broad and so can be present across a large number of image spectral channels. The intensity of AF also increases as the excitation wavelength is decreased, causing increasing amounts of autofluorescence when exciting in the blue and near-UV range of the spectrum (400 - 500 nm). This thesis reports the development of hyperspectral, fluorescence and brightfield imaging of entire, paraffin-embedded, formalin-fixed (PEFF) tissue slides using a prototype confocal scanner with a large field of view (FOV). This technology addresses the challenges of imaging large tissue sections through the use of a telecentric f-theta laser scan lens thus allowing an entire microscope slide (22x70 mm) to be imaged in a single scan at resolution equivalent to a 10x microscope objective. The development and optimization of brightfield and single-channel fluorescence imaging modes are discussed in the first half of this thesis, while the second half and appendices concentrate on the spectral properties of the system and removal of AF from PEFF tissue sections. The hyperspectral imaging mode designed for this system allows the fluorescence emission spectrum of each image pixel to be sampled at 6.7 nm/channel over a spectral range of 500-700 nm. This results in the ability to separate distinct fluorescence signatures from each other, and enables quantification even in situations where the AF completely masks the signal from the applied labels.

Dans cette thèse, nous avons présenté les aspects de la technologie d'imagerie hyperspectrale en concentrant sur le problème de démélange non-linéaire. Pour cette tâche, nous avons proposé trois solutions. La première consiste à intégrer les avantages de l'apprentissage de variétés dans les méthodes de démélange classique pour concevoir leurs versions non-linéaires. Les résultats avec les données générées sur une variété bien connue - le "Swissroll"- donne des résultats prometteurs. Les méthodes fonctionnent beaucoup mieux avec l'augmentation de la non-linéarité. Cependant, l'absence de contrainte de non-négativité dans ces méthodes reste une question ouverte pour des améliorations à trouver. La deuxième proposition vise à utiliser la méthode de pré-image pour estimer une transformation inverse de l'espace de données entrées des pixels vers l'espace des abondances. L'ajout des informations spatiales sous forme "variation totale" est également introduit pour rendre l'algorithme plus robuste au bruit. Néanmoins, le problème d'obtention des données de réalité terrain nécessaires pour l'étape d'apprentissage limite l'application de ce type d'algorithmes. / In this thesis , we present several aspects of hyperspectral imaging technology , while focusing on the problem of non- linear unmixing . We have proposed three solutions for this task. The first one is integrating the advantages of manifold learning in classical unmixing methods to design their nonlinear versions . Results with data generated on a well-known manifold- the " Swissroll " - seem promising. The methods work much better with the increase in non- linearity compared with their linear version. However, the absence of constraint of non- negativity in these methods remains an open question for improvements . The second proposal is using the pre-image method for estimating an inverse transformation of the data form pixel space to abundance of space . The adoption of spatial information as " total variation " is also introduced to make the algorithm more robust to noise . However, the problem of obtaining ground truth data required for learning step limits the application of such algorithms.

La télédétection en imagerie hyperspectrale infrarouge thermique est l'étude d'images en luminance, acquises depuis un avion ou un satellite dans le domaine spectral de l'infrarouge thermique. Ces images sont liées à l'émissivité et à la température, estimées par les méthodes de découplage température/émissivité (T/E), ainsi qu'à l'abondance, estimée par les méthodes de démélange, des matériaux présents dans la scène. Si les méthodes de découplage T/E ont été largement étudiées, les méthodes de démélange dans ce domaine spectral restent peu explorées : c'est l'objectif de cette thèse. Pour cela, nous avons mis en place trois stratégies de démélange. Dans un premier temps, le démélange est effectué sur les luminances. Cette stratégie donne globalement de bons résultats mais est relativement sensible aux variations spatiales de la température. La deuxième stratégie, démélangeant à partir des estimations d'émissivité des méthodes de découplage T/E, s'affranchit de cette variation spatiale mais donne des résultats plus bruités. Enfin, une méthode de démélange basée sur l'estimation conjointe de la température et des abondances a été élaborée. Cette méthode s'appelle Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST) et donne de meilleurs résultats que la première stratégie tout en étant robuste aux variations spatiales de la température. / Thermal hyperspectral remote sensing provides information about materials from the measured radiance image. It is achieved using temperature and emissivity separation (TES) methods, estimating the emissivity and the temperature of the materials, and using unmixing methods, estimating their abundances. TES methods have been well investigated while too few studies have been working on unmixing in thermal infrared domain : this is the objective of this PhD. Therefore, three strategies have been studied. First, the unmixing is applied on radiance. It achieves good results but depends on the spatial variation of temperature. Applying the unmixing on the emissivities, estimated using the TES methods, gets rid of the spatial variation of temperature but provides a noisy abundance estimation. Eventually, a new method called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST) is designed to jointly estimate the abundance and the temperature of materials within the pixels. It gives better results than the first strategy and is more robust to spatial variation of temperature.