O objetivo deste trabalho é apresentar um novo método para a remoção do ruído Poisson em imagens de mamografia digital adquiridas com baixa dosagem de radiação. Sabe-se que a mamografia por raios X é o exame mais eficiente para a detecção precoce do câncer de mama, aumentando consideravelmente as chances de cura da doença. No entanto, a radiação absorvida pela paciente durante o exame ainda é um problema a ser tratado. Estudos indicam que a exposição à radiação pode induzir a formação do câncer em algumas mulheres radiografadas. Apesar desse número ser significativamente baixo em relação ao número de mulheres que são salvas pelo exame, existe a necessidade do desenvolvimento de meios que viabilizem a diminuição da dose de radiação empregada. No entanto, uma redução na dose de radiação piora a qualidade da imagem pela diminuição da relação sinal-ruído, prejudicando o diagnóstico médico e a detecção precoce da doença. Nesse sentido, a proposta deste trabalho é apresentar um método para a filtragem do ruído Poisson que é adicionado às das imagens mamográficas quando adquiridas com baixa dosagem de radiação, fazendo com que ela apresente qualidade equivalente àquela adquirida com a dose padrão de radiação. O algoritmo proposto foi desenvolvido baseado em adaptações de algoritmos bem estabelecidos na literatura, como a filtragem no domínio Wavelet, aqui usando o Shrink-thresholding (WTST), e o Block-matching and 3D Filtering (BM3D). Os resultados obtidos com imagens mamográficas adquiridas com phantom e também imagens clínicas, mostraram que o método proposto é capaz de filtrar o ruído adicional incorporado nas imagens sem perda aparente de informação. / The aim of this work is to present a novel method for removing the Poisson noise in digital mammography images acquired with reduced radiation dose. It is known that the X-ray mammography is the most effective exam for early detection of breast cancer, greatly increasing the chances of healing the disease. However, the radiation absorbed by the patient during the exam is still a problem to be treated. Some studies showed that mammography can induce breast cancer in a few women. Although this number is significantly low compared to the number of women who are saved by the exam, it is important to develop methods to enable the reduction of the radiation dose used in the exam. However, dose reduction led to a decrease in image quality by means of the signal to noise ratio, impairing medical diagnosis and the early detection of the disease. In this sense, the purpose of this study is to propose a new method to reduce Poisson noise in mammographic images acquired with low radiation dose, in order to achive the same quality as those acquired with the standard dose. The method is based on well established algorithms in the literature as the filtering in Wavelet domain, here using Shrink-thresholding (WTST) and the Block-matching and 3D Filtering (BM3D). Results using phantom and clinical images showed that the proposed algorithm is capable of filtering the additional noise in images without apparent loss of information.

O objetivo deste trabalho é apresentar um novo método para a remoção do ruído Poisson em imagens de mamografia digital adquiridas com baixa dosagem de radiação. Sabe-se que a mamografia por raios X é o exame mais eficiente para a detecção precoce do câncer de mama, aumentando consideravelmente as chances de cura da doença. No entanto, a radiação absorvida pela paciente durante o exame ainda é um problema a ser tratado. Estudos indicam que a exposição à radiação pode induzir a formação do câncer em algumas mulheres radiografadas. Apesar desse número ser significativamente baixo em relação ao número de mulheres que são salvas pelo exame, existe a necessidade do desenvolvimento de meios que viabilizem a diminuição da dose de radiação empregada. No entanto, uma redução na dose de radiação piora a qualidade da imagem pela diminuição da relação sinal-ruído, prejudicando o diagnóstico médico e a detecção precoce da doença. Nesse sentido, a proposta deste trabalho é apresentar um método para a filtragem do ruído Poisson que é adicionado às das imagens mamográficas quando adquiridas com baixa dosagem de radiação, fazendo com que ela apresente qualidade equivalente àquela adquirida com a dose padrão de radiação. O algoritmo proposto foi desenvolvido baseado em adaptações de algoritmos bem estabelecidos na literatura, como a filtragem no domínio Wavelet, aqui usando o Shrink-thresholding (WTST), e o Block-matching and 3D Filtering (BM3D). Os resultados obtidos com imagens mamográficas adquiridas com phantom e também imagens clínicas, mostraram que o método proposto é capaz de filtrar o ruído adicional incorporado nas imagens sem perda aparente de informação. / The aim of this work is to present a novel method for removing the Poisson noise in digital mammography images acquired with reduced radiation dose. It is known that the X-ray mammography is the most effective exam for early detection of breast cancer, greatly increasing the chances of healing the disease. However, the radiation absorbed by the patient during the exam is still a problem to be treated. Some studies showed that mammography can induce breast cancer in a few women. Although this number is significantly low compared to the number of women who are saved by the exam, it is important to develop methods to enable the reduction of the radiation dose used in the exam. However, dose reduction led to a decrease in image quality by means of the signal to noise ratio, impairing medical diagnosis and the early detection of the disease. In this sense, the purpose of this study is to propose a new method to reduce Poisson noise in mammographic images acquired with low radiation dose, in order to achive the same quality as those acquired with the standard dose. The method is based on well established algorithms in the literature as the filtering in Wavelet domain, here using Shrink-thresholding (WTST) and the Block-matching and 3D Filtering (BM3D). Results using phantom and clinical images showed that the proposed algorithm is capable of filtering the additional noise in images without apparent loss of information.

Extraire des mesures sur des surfaces est un problème dans de nombreux domaines de recherche. L'archaïsme de certains systèmes ou la cherté d'appareils perfectionnés empêchent l'extraction rapide et robuste de ces paramètres. Pourtant, ils sont essentiels dans de nombreux domaines, comme les paramètres de rugosité qui interviennent dans de nombreux phénomènes physiques ou les valeurs dendrométriques pour l'étude de la bio-diversité. En parallèle, l'utilisation et la production de contenus 3D se développent considérablement ces dernières années dans des domaines très divers. Le but de cette thèse est donc d'utiliser toutes ces innovations dans le domaine de la reconstruction 3D et de les appliquer à la mesure de paramètres de surfaces. Pour cela, il est nécessaire de créer une chaîne complète de reconstruction 3D, n'utilisant que des images prises sans contrainte dans le but d'être accessible au plus grand nombre. Dans cette chaîne nous utilisons des algorithmes de stéréo-vision robustes produisant, pour chaque couple d'images, un nuage de points. Après le passage de ces nuages dans un référentiel commun, une étape de filtrage des points 3D et de suppression des redondances est nécessaire. Une étape de lissage permet d'obtenir le nuage final. Pour justifier des bons résultats obtenus, une étape de validation nous a permis de vérifier et d'étudier la robustesse de la chaîne de traitements développée. Enfin, les paramètres de rugosités et dendrométriques seront extraits. Nous étudierons dans ces deux cas, comment extraire ces informations et leurs utilisations / Extracting measures on surfaces is a problem in many areas of research. The archaism of some systems or the costliness of sophisticated devices prevent the fast and robust extraction of these parameters. Yet these measures are essential in many areas, such as roughness parameters involved in many physical phenomena or dendrometric values for the study of biodiversity. In parallel, the use and production of 3D content has grown dramatically this past year in very diverse domains. The purpose of this thesis is to use these innovations in the context of surfaces parameter measurements. It is necessary to create a complete chain of 3D reconstruction, using pictures taken without constraint, in order to be open to as many people. This chain uses robust stereo-vision algorithms in order to produce a point cloud for each pair of images. After the generation of these point cloud in the same geometric frame, a filtering step of 3D points and a deletion step of redundancies are necessary and a smoothing step allows us to obtain the final point cloud. To reveal the good results, a validation step has enabled us to verify and investigate the robustness of the developed chain. The roughness and dendrometric parameters are finally extracted. We will study in both cases, how to extract this information and their uses

The osteocytes are the most abundant and longest living bone cells, embedded in the bone matrix. They are interconnected with each other through dendrites, located in slender canals called canaliculi. The osteocyte lacunae, cavities in which the cells are located, together with the canaliculi form a communication network throughout the bone matrix, permitting transport of nutrients, waste and signals. These cells were firstly considered passive, but lately it has become increasingly clear their role as mechanosensory cells and orchestrators of bone remodeling. Despite recent advances in imaging techniques, none of the available methods can provide an adequate 3D assessment of the lacuno-canalicular network (LCN). The aims of this thesis were to achieve 3D imaging of the LCN with synchrotron radiation X-ray computed tomography (SR-CT) and to develop tools for 3D detection and segmentation of this cell network, leading towards automatic quantification of this structure. We demonstrate the feasibility of parallel beam SR-CT to image in 3D the LCN (voxel~300 nm). This technique can provide data on both the morphology of the cell network and the composition of the bone matrix. Compared to the other 3D imaging methods, this enables imaging of tissue covering a number of cell lacunae three orders of magnitude greater, in a simpler and faster way. This makes possible the study of sets of specimens in order to reach biomedical conclusions. Furthermore, we propose the use of divergent holotomography, to image the ultrastructure of bone tissue (voxel~60 nm). The image reconstruction provides phase maps, obtained after the application of a suitable phase retrieval algorithm. This technique permits assessment of the cell network with higher accuracy and it enables the 3D organization of collagen fibres organization in the bone matrix, to be visualized for the first time. In order to obtain quantitative parameters on the geometry of the cell network, this has to be segmented. Due to the limitations in spatial resolution, canaliculi appear as 3D tube-like structures measuring only 1-3 voxels in diameter. This, combined with the noise, the low contrast and the large size of each image (8 GB), makes the segmentation a difficult task. We propose an image enhancement method, based on a 3D line filter combined with bilateral filtering. This enables improvement in canaliculi detection, reduction of the background noise and cell lacunae preservation. For the image segmentation we developed a method based on variational region growing. We propose two expressions for energy functionals to minimize in order to detect the desired structure, based on the 3D line filter map and the original image. Preliminary quantitative results on human femoral samples are obtained based on connected components analysis and a few observations related to the bone cell network and its relation with the bone matrix are presented.

Les ostéocytes sont les plus nombreuses cellules du tissu osseux, enterrées dans la matrice osseuse. Elles sont interconnectées par des dendrites, situées dans des canaux appelés canalicules. Les lacunes ostéocytaires, les cavités dans lesquelles les cellules sont logées, avec les canalicules forment un réseau de communication à travers la matrice osseuse, permettant le transport des nutriments et des signaux. Ces cellules, considérées d’abord passives, ont révélé dernièrement leur rôle en tant que cellules mécanosensitives et orchestratrices du remodelage osseux. Malgré les progrès récents des techniques d'imagerie, aucune méthode disponible ne fournit une évaluation 3D adéquate du réseau lacuno-canaliculaire (LCN). Les objectifs de cette thèse ont porté sur l’imagerie 3D du LCN par tomographie synchrotron à rayons X (SR-CT), et le développement d’outils de détection et segmentation 3D de ce réseau cellulaire, afin de le quantifier et analyser. Nous démontrons la faisabilité de la SR-CT en géométrie parallèle pour imager le LCN dans le tissu osseux (voxel~300nm). Cette technique fournit des données 3D sur la morphologie du réseau cellulaire et aussi sur la composition de la matrice osseuse. Comparée aux méthodes d'imagerie 3D existantes, la SR-CT permet l'imagerie d’un volume de tissu beaucoup plus important, d'une manière plus simple et rapide. Cela rend possible l'étude de séries de spécimens afin d'obtenir des conclusions biomédicales. Nous proposons aussi l'utilisation de l’holotomographie divergente synchrotron, pour imager l'ultrastructure du tissu osseux (voxel~60nm). La reconstruction d'image fournit des cartes de phase, obtenues après application d'un algorithme d’inversion de phase adéquat. Cette technique a permis l'évaluation du réseau cellulaire avec une précision plus élevée et de visualiser, pour la première fois en 3D, l'organisation des fibres de collagène. Afin d'obtenir des résultats quantitatifs sur la géométrie du réseau cellulaire, celui doit être segmenté. À cause des limitations de la résolution spatiale, les canalicules apparaissent comme de structures tubulaires très fines (diamètre 1-3 voxels). Ceci, combiné avec le bruit, le faible contraste et la grande taille des images (8Go), rendent la segmentation difficile. Nous proposons une méthode de filtrage non-linéaire 3D, basée sur le rehaussement des structures linéaires, combiné avec un filtrage bilatéral. Cela permet une amélioration de la détection des canalicules, la réduction du bruit de fond et de la préservation des lacunes cellulaires. Pour la segmentation d'images, nous avons développé une méthode basée sur la croissance de région variationnelle. Nous proposons deux expressions de fonctionnelles d'énergie à minimiser, afin de détecter la structure souhaitée. Des résultats quantitatifs préliminaires sont obtenus à partir d’une analyse en composantes connexes sur des échantillons humaines et des observations relatives au réseau ostéocytaire sont présentés. / The osteocytes are the most abundant and longest living bone cells, embedded in the bone matrix. They are interconnected with each other through dendrites, located in slender canals called canaliculi. The osteocyte lacunae, cavities in which the cells are located, together with the canaliculi form a communication network throughout the bone matrix, permitting transport of nutrients, waste and signals. These cells were firstly considered passive, but lately it has become increasingly clear their role as mechanosensory cells and orchestrators of bone remodeling. Despite recent advances in imaging techniques, none of the available methods can provide an adequate 3D assessment of the lacuno-canalicular network (LCN). The aims of this thesis were to achieve 3D imaging of the LCN with synchrotron radiation X-ray computed tomography (SR-CT) and to develop tools for 3D detection and segmentation of this cell network, leading towards automatic quantification of this structure. We demonstrate the feasibility of parallel beam SR-CT to image in 3D the LCN (voxel~300 nm). This technique can provide data on both the morphology of the cell network and the composition of the bone matrix. Compared to the other 3D imaging methods, this enables imaging of tissue covering a number of cell lacunae three orders of magnitude greater, in a simpler and faster way. This makes possible the study of sets of specimens in order to reach biomedical conclusions. Furthermore, we propose the use of divergent holotomography, to image the ultrastructure of bone tissue (voxel~60 nm). The image reconstruction provides phase maps, obtained after the application of a suitable phase retrieval algorithm. This technique permits assessment of the cell network with higher accuracy and it enables the 3D organization of collagen fibres organization in the bone matrix, to be visualized for the first time. In order to obtain quantitative parameters on the geometry of the cell network, this has to be segmented. Due to the limitations in spatial resolution, canaliculi appear as 3D tube-like structures measuring only 1-3 voxels in diameter. This, combined with the noise, the low contrast and the large size of each image (8 GB), makes the segmentation a difficult task. We propose an image enhancement method, based on a 3D line filter combined with bilateral filtering. This enables improvement in canaliculi detection, reduction of the background noise and cell lacunae preservation. For the image segmentation we developed a method based on variational region growing. We propose two expressions for energy functionals to minimize in order to detect the desired structure, based on the 3D line filter map and the original image. Preliminary quantitative results on human femoral samples are obtained based on connected components analysis and a few observations related to the bone cell network and its relation with the bone matrix are presented.