Magnesium alloys have poor formability at room temperature, due to a limited number of slip systems owing to the hexagonal closed packed structure of magnesium. One possibility to increase the formability of magnesium alloys is to refine the grain size. A fine grain magnesium alloy shows high strength and high ductility at room temperature, hence an improved formability. In addition to grain refinement, the formability of Mg alloys can be improved by controlling crystallographic texture. Severe plastic deformation (SPD) processes namely, equal channel angular pressing (ECAP) and multi-axial forging (MAF) have led to improvement in room temperature mechanical property of magnesium alloys. Further, it has been reported that by adding rare earth elements, room temperature ductility is enhanced to nearly 30%. The increase in property is attributed to crystallographic texture. Many rare earth elements have been added to magnesium alloys and new alloy systems have been developed. Amongst these elements, Ce addition has been shown to enhance the tensile ductility in rolled sheets at room temperature by causing homogeneous deformation. It has been observed that processing of rare-earth containing alloys below 300°C is difficult. Processing at higher temperatures leads to grain growth which ultimately leads to low strength at room temperature. The present thesis is an attempt to combine the effect SPD and rare earth addition, and to examine the overall effect on microstructure and texture, hence on room temperature mechanical properties. In this thesis, Mg-0.2%Ce alloy has been studied with regard to the two SPD processes, namely, ECAP and MAF. The thesis has been divided into six chapters. Chapter 1 is dedicated to introduction and literature review pertaining to different severe plastic deformation processes as applied to different Mg alloys. Chapter 2 includes the details of experimental techniques and characterization procedures, which are commonly employed for the entire work. Chapter 3 addresses the effect of ECAP on the evolution of texture and microstructure in Mg-0.2%Ce alloy. ECAP has been carried out on two different initial microstructure and texture in the starting condition, namely forged and extruded. ECAP has been successfully carried out for the forged billets at 250°C while cracks get developed in the extruded billet when ECAP was done at 250°C. The difference in the deformation behaviour of the two alloys has been explained on the basis of the crystallographic texture of the initial materials. The microstructure of the ECAP materials indicates the occurrence of recrystallization. The recrystallization mechanism is identified as “continuous dynamic recovery and recrystallization” (CDRR) and is characterized by a rotation of the deformed grains by ~30⁰ along c-axis. The yield strengths and ductility of the two ECAP materials have been found quite close. However, there is a difference in the yield strength as well as ductility values when the materials were tested under compression. The extruded billet has the tension compression asymmetry ~1.7 while the forged material has the asymmetry as ~2.2. After ECAP, the yield asymmetry reduces to ~1 for initially extruded billet, while for the initially forged billet the yield asymmetry value reduces to ~1.9. In chapter 4, the evolution of microstructure and texture was examined using another severe plastic deformation technique, namely multi axial forging (MAF). In this process, the material was plastically deformed by plane strain compression subsequently along all three axes. In this case also two different initial microstructures and texture were studied, namely the material in as cast condition and the extruded material. The choice of initial materials in this case was done in order to examine the effect of different initial grain size in addition to different textures. By this method, the alloy Mg-0.2%Ce could be deformed without fracture at a minimum temperature of 350⁰C leading to fine grain size (~3.5 µm) and a weak texture. Grain refinement was more in the initial cast billets compared to the initial extruded billet after processing. The mechanism of grain refinement has been identified as twin assisted dynamic recrystallization (TDRX) and CDRR type. The mechanical properties under tension as well as under compression were also evaluated in the present case. The initially extruded billet has shown low tension compression asymmetry (~1.2) than cast billet (~1.9), after MAF. Chapter 5 addresses the exclusive effect of texture on room temperature tensile properties of the alloy. Different textures were the outcomes of ECAP and MAF processes. In this case, in order to obtain an exact role of texture, a third of deformation mode, rolling, was also introduced. All the processed materials were annealed to obtain similar grain size but different texture. A similar strength and ductility for ECAP and MAF, where the textures were qualitatively very different, was attributed to the fact that texture of both the ECAP and MAF processed materials, was away from the ideal end orientation for tensile tests. In chapter 7, the final outcomes of the thesis have been summarized and scope for the future work has been presented.

O disco intervertebral é uma estrutura cuja função é receber, amortecer e distribuir o impacto das cargas impostas sobre a coluna vertebral. O aumento da idade e a postura adotada pelo indivíduo podem levar à degeneração do disco intervertebral. Atualmente, a Ressonância Magnética (RM) é considerada o melhor e mais sensível método não invasivo de avaliação por imagem do disco intervertebral. Neste trabalho foram desenvolvidos métodos quantitativos computadorizados para auxílio ao diagnóstico da degeneração do disco intervertebral em imagens de ressonância magnética ponderadas em T2 da coluna lombar, de acordo com a escala de Pfirrmann, uma escala semi-quantitativa, com cinco graus de degeneração. Os algoritmos computacionais foram testados em um conjunto de dados que consiste de imagens de 300 discos, obtidos de 102 indivíduos, com diferentes graus de degeneração. Máscaras binárias de discos segmentados manualmente foram utilizadas para calcular seus centroides, visando criar um ponto de referência para possibilitar a extração de atributos. Uma análise de textura foi realizada utilizando a abordagem proposta por Haralick. Para caracterização de forma, também foram calculados os momentos invariantes definidos por Hu e os momentos centrais para cada disco. A classificação do grau de degeneração foi realizada utilizando uma rede neural artificial e o conjunto de atributos extraídos de cada disco. Uma taxa média de acerto na classificação de 87%, com erro padrão de 6,59% e uma área média sob a curva ROC (Receiver Operating Characteristic) de 0,92 indicam o potencial de aplicação dos algoritmos desenvolvidos como ferramenta de apoio ao diagnóstico da degeneração do disco intervertebral. / The intervertebral disc is a structure whose function is to receive, absorb and transmit the impact loads imposed on the spine. Increasing age and the posture adopted by the individual can lead to degeneration of the intervertebral disc. Currently, Magnetic Resonance Imaging (MRI) is considered the best and most sensitive noninvasive method to imaging evaluation of the intervertebral disc. In this work were developed methods for quantitative computer-aided diagnosis of the intervertebral disc degeneration in MRI T2 weighted images of the lumbar column according to Pfirrmann scale, a semi-quantitative scale with five degrees of degeneration. The algorithms were tested on a dataset of 300 images obtained from 102 subjects with varying degrees of degeneration. Binary masks manually segmented of the discs were used to calculate their centroids, to create a reference point to enable extraction of attributes. A texture analysis was performed using the approach proposed by Haralick. For the shape characterization, invariant moments defined by Hu and central moments were also calculated for each disc. The rating of the degree of degeneration was performed using an artificial neural network and the set of extracted attributes of each disk. An average rate of correct classification of 87%, with standard error 6.59% and an average area under the ROC curve (Receiver Operating Characteristic) of 0.92 indicates the potential application of the algorithms developed as a diagnostic support tool to the degeneration of the intervertebral disc.

The thesis is focused on detection of local disappearance of the neural layer on retina in fundus-camera images. The first chapter describes the human eye physiology, the glaucoma disease and the analyzed data. The second chapter compares four different approaches that should enable automatic detection of a possible damage to the retinal neural layer. These four approaches have been tested and evaluated; three of them showed an acceptable correlation with the medical expert conclusions – the directional spectral approach, the edge based approach and the difference local brightness. The last approch via local co-occurrence matrices has not turned out to be informative with the respect to the issue concerned. Then a program for the automatic detection of the nerve fibre layer loss areas has been designed, realized and evaluated. This task is solved in the last chapter. A relatively good agreement between the medical expert conclusions and the conclusions detected automatically by this program has been reached.

This work describes completely new approach to detection of retinal nerve fibre layer (RNFL) loss in colour fundus images. Such RNFL losses indicate eye glaucoma illness and an early diagnosis of RNFL changes is very important for successful treatment. Method is presented with the purpose of supporting glaucoma diagnosis in ophthalmology. The proposed textural analysis method utilizes local binary patterns (LBP). This approach is characterized especially by computational simplicity and insensitivity to monotonic changes of illumination. Image histograms of LBP distributions are used to gain several textural features aimed to classify healthy or glaucomatous tissue of the retina. The method was experimentally tested using fundus images of glaucomatous patients with focal RNFL loss. The results show that the proposed method can be used in order to supporting diagnosis of glaucoma with satisfactory efficiency.

Goal of this work is to theoretically develop and then program a system in Matlab environment to be used as a detection tool for layer of retinal neuron pathways . First part engages oneself upon the problem of analysis within spectral plane and results of using filters conceived upon statistical occurrences of certain frequencies in used samples. Second part than deals with use of gabor filters to detect neuron pathways and the statistical results gained by their use. Based on the results an analysis tool was programmed.

This paper presents basic principles of optical coherence tomography, review of applications and basic categorization of these systems. Paper also deals with the typical properties of images from optical coherence tomography, especially speckle pattern. This paper also provides an overview of the origin of speckle noise and utilization of its dependence on microstructure of probed tissue for image classification based on textural analysis. Experimental part of this paper consists of phantom preparation, data acquisition by OCT system, implementation of speckle analysis in MATLAB and of testing of its functionality on standard textural dataset and also on acquired image phantom data. Speckle analysis is used for phantom image data segmentation.

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.

This thesis deals with image analysis methods which can be exploited in exoscopic analysis of sedimentary grains, specifically for the purpose of distinguishing between geomorphologic geneses which influenced a form of sedimentary grains. The images of sedimentary grains were acquired by a scanning electron microscope. The main contribution is the proposal of multiple methods that can significantly automate the exoscopic analysis. These methods cover the automatic segmentation of grains in image, the automatic analysis of roundness of 2D grain projection and the classification of geomorphologic geneses according to the grain surface structure. In the section concerning the automatic segmentation, a segmentation method enabling an easy subsequent manual result correction was proposed. This method is based on the split-and-merge approach. The individual steps the procedure were designed to exploit specific properties of sedimentary grain images in order to obtain the best segmentation results. In the section concerning the automatic roundness analysis of 2D projection of sedimentary grains, an influence of pixel resolution on a result roundness value was evaluated. Further, a minimal number of grains, which is necessary to analyze in order to reliably compare a pair of geomorphological geneses, was investigated. For the determination of this number, a method was proposed and experimentally verified. In the section of automatic analysis of sedimentary grain surface structure, a method for classification of geomorphologic geneses was proposed. The method utilizes low-level texture features which describes individual images of sedimentary grains. A model of geomorphological genesis is constituted of a set of histograms representing occurrences of different configurations of low-level texture features. The methods proposed in the thesis were tested and evaluated based on a database, which consists of sedimentary grain samples from 4 different geomorphological geneses (eolic, glacial, slope and volcanic).

This thesis deals with the segmentation and classification of paraspinal muscle and subcutaneous adipose tissue in 3D CT image data in order to use them subsequently as internal calibration phantoms to measure bone mineral density of a vertebrae. Chosen methods were tested and afterwards evaluated in terms of correctness of the classification and total functionality for subsequent BMD value calculation. Algorithms were tested in programming environment Matlab® on created patient database which contains lumbar spines of twelve patients. Following sections of this thesis contain theoretical research of the issue of measuring bone mineral density, segmentation and classification methods and description of practical part of this work.

La pathologie numérique s’est développée ces dernières années grâce à l’avancée récente des algorithmes d’analyse d’images et de la puissance de calcul. Notamment, elle se base de plus en plus sur les images histologiques. Ce format de données a la particularité de révéler les objets biologiques recherchés par les experts en utilisant des marqueurs spécifiques tout en conservant la plus intacte possible l’architecture du tissu. De nombreuses méthodes d’aide au diagnostic à partir de ces images se sont récemment développées afin de guider les pathologistes avec des mesures quantitatives dans l’établissement d’un diagnostic. Les travaux présentés dans cette thèse visent à adresser les défis liés à l’analyse d’images histologiques, et à développer un modèle d’aide au diagnostic se basant principalement sur les relations spatiales, une information que les méthodes existantes n’exploitent que rarement. Une technique d’analyse de la texture à plusieurs échelles est tout d’abord proposée afin de détecter la présence de tissu malades dans les images. Un descripteur d’objets, baptisé Force Histogram Decomposition (FHD), est ensuite introduit dans le but d’extraire les formes et l’organisation spatiale des régions définissant un objet. Finalement, les images histologiques sont décrites par les FHD mesurées à partir de leurs différents types de tissus et des objets biologiques marqués qu’ils contiennent. Les expérimentations intermédiaires ont montré que les FHD parviennent à correctement reconnaitre des objets sur fonds uniformes y compris dans les cas où les relations spatiales ne contiennent à priori pas d’informations pertinentes. De même, la méthode d’analyse de la texture s’avère satisfaisante dans deux types d’applications médicales différents, les images histologiques et celles de fond d’œil, et ses performances sont mises en évidence au travers d’une comparaison avec les méthodes similaires classiquement utilisées pour l’aide au diagnostic. Enfin, la méthode dans son ensemble a été appliquée à l’aide au diagnostic pour établir la sévérité d’un cancer via deux ensembles d’images histologiques, un de foies métastasés de souris dans le contexte du projet ANR SPIRIT, et l’autre de seins humains dans le cadre du challenge CPR 2014 : Nuclear Atypia. L’analyse des relations spatiales et des formes à deux échelles parvient à correctement reconnaitre les grades du cancer métastasé dans 87, 0 % des cas et fourni des indications quant au degré d’atypie nucléaire. Ce qui prouve de fait l’efficacité de la méthode et l’intérêt d’encoder l’organisation spatiale dans ce type d’images particulier. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. Particularly, it is more and more based on histology images. This modality of images presents the advantage of showing only the biological objects targeted by the pathologists using specific stains while preserving as unharmed as possible the tissue structure. Numerous computer-aided diagnosis methods using these images have been developed this past few years in order to assist the medical experts with quantitative measurements. The studies presented in this thesis aim at adressing the challenges related to histology image analysis, as well as at developing an assisted diagnosis model mainly based on spatial relations, an information that currently used methods rarely use. A multiscale texture analysis is first proposed and applied to detect the presence of diseased tissue. A descriptor named Force Histogram Decomposition (FHD) is then introduced in order to extract the shapes and spatial organisation of regions within an object. Finally, histology images are described by the FHD measured on their different types of tissue and also on the stained biological objects inside every types of tissue. Preliminary studies showed that the FHD are able to accurately recognise objects on uniform backgrounds, including when spatial relations are supposed to hold no relevant information. Besides, the texture analysis method proved to be satisfactory in two different medical applications, namely histology images and fundus photographies. The performance of these methods are highlighted by a comparison with the usual approaches in their respectives fields. Finally, the complete method has been applied to assess the severity of cancers on two sets of histology images. The first one is given as part of the ANR project SPIRIT and presents metastatic mice livers. The other one comes from the challenge ICPR 2014 : Nuclear Atypia and contains human breast tissues. The analysis of spatial relations and shapes at two different scales achieves a correct recognition of metastatic cancer grades of 87.0 % and gives insight about the nuclear atypia grade. This proves the efficiency of the method as well as the relevance of measuring the spatial organisation in this particular type of images.