Super-Resolution Image Reconstruction Using Non-Linear Filtering Techniques

Super-resolution (SR) is a filtering technique that combines a sequence of under-sampled and degraded low-resolution images to produce an image at a higher resolution. The reconstruction takes advantage of the additional spatio-temporal data available in the sequence of images portraying the same scene. The fundamental problem addressed in super-resolution is a typical example of an inverse problem, wherein multiple low-resolution (LR)images are used to solve for the original high-resolution (HR) image. Super-resolution has already proved useful in many practical cases where multiple frames of the same scene can be obtained, including medical applications, satellite imaging and astronomical observatories. The application of super resolution filtering in consumer cameras and mobile devices shall be possible in the future, especially that the computational and memory resources in these devices are increasing all the time. For that goal, several research problems need to be ...

Trimeche, Mejdi — Tampere University of Technology

Unsupervised and semi-supervised Non-negative Matrix Factorization methods for brain tumor segmentation using multi-parametric MRI data

Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. The 5 year survival rate of the most common and most malignant subtype, i.e. glioblastoma, is about 5%. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor patients. Conventional MRI (cMRI) provides excellent soft tissue contrast without exposing the patient to potentially harmful ionizing radiation. Over the past decade, advanced MRI modalities, such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have gained interest in the clinical field, and their added value regarding brain tumor diagnosis, treatment planning and follow-up has been recognized. Tumor segmentation involves the imaging-based delineation of a tumor and its subcompartments. In gliomas, segmentation plays an important role in treatment planning as well ...

Sauwen, Nicolas — KU Leuven

Spatiotonal Adaptivity in Super-Resolution of under-sampled Image Sequences

This thesis concerns the use of spatial and tonal adaptivity in improving the resolution of aliased image sequences under scene or camera motion. Each of the five content chapters focuses on a different subtopic of super-resolution: image registration (chapter 2), image fusion (chapter 3 and 4), super-resolution restoration (chapter 5), and super-resolution synthesis (chapter 6). Chapter 2 derives the Cramer-Rao lower bound of image registration and shows that iterative gradient-based estimators achieve this performance limit. Chapter 3 presents an algorithm for image fusion of irregularly sampled and uncertain data using robust normalized convolution. The size and shape of the fusion kernel is adapted to local curvilinear structures in the image. Each data sample is assigned an intensity-related certainty value to limit the influence of outliers. Chapter 4 presents two fast implementations of the signal-adaptive bilateral filter. The xy-separable implementation filters ...

Pham, Tuan Q. — Delft University of Technology

Classification of brain tumors based on magnetic resonance spectroscopy

Nowadays, diagnosis and treatment of brain tumors is based on clinical symptoms, radiological appearance, and often histopathology. Magnetic resonance imaging (MRI) is a major noninvasive tool for the anatomical assessment of tumors in the brain. However, several diagnostic questions, such as the type and grade of the tumor, are difficult to address using MRI. The histopathology of a tissue specimen remains the gold standard, despite the associated risks of surgery to obtain a biopsy. In recent years, the use of magnetic resonance spectroscopy (MRS), which provides a metabolic profile, has gained a lot of interest for a more detailed and specific noninvasive evaluation of brain tumors. In particular, magnetic resonance spectroscopic imaging (MRSI) is attractive as this may also enable to visualize the heterogeneous spatial extent of tumors, both inside and outside the MRI detectable lesion. As manual, individual, viewing ...

Luts, Jan — Katholieke Universiteit Leuven

Advanced signal processing for magnetic resonance spectroscopy

Assertive diagnosis of cancer, Alzheimer’s disease, epilepsy and other metabolic diseases is essential to provide patients with the adequate treatment. Recently, different invasive and non-invasive techniques have been developed for this purpose, nevertheless, due to their harmless properties the non-invasive techniques have gained more value. Magnetic Resonance is a well-known non-invasive technique that provides spectra (metabolite peaks) and images (anatomical structures) of the examined tissue. In Magnetic Resonance Spectroscopy (MRS), molecules containing certain excitable nuclei, such as 1H, provide the metabolite information. As a consequence, the peaks in the MR spectra correspond to observable metabolites which are the biomarkers of diseases. Finally, metabolite concentrations are computed and compared against normal values in order to establish the diagnosis. The method to obtain such amplitudes is also called quantification and its accuracy is essential for diagnosis assessment. Quantification of MRS signals is ...

Osorio Garcia, Maria Isabel — KU Leuven

Signal processing and classification for magnetic resonance spectroscopic data with clinical applications

Over the last decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of human body and it is widely used in clinical diagnosis. In vivo and ex vivo Magnetic Resonance Spectroscopic (MRS) techniques can additionally provide valuable metabolic information as compared to MRI and are gaining more clinical interest. The analysis of MRS data is a complex procedure and requires several preprocessing steps aiming to improve the quality of the data and to extract the most relevant features before any classification algorithm can be successfully applied. In this thesis a new approach to quantify magnetic resonance spectroscopic imaging (MRSI) data and therefore to obtain improved metabolite estimates is proposed. Then an important part is focusing on improving the diagnosis of glial brain tumors which are characterized by an extensive heterogeneity since various intramural histopathological properties such ...

Croitor Sava, Anca Ramona — KU Leuven

Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis

Diffusion Magnetic Resonance Imaging (dMRI) is a meaningful technique for white matter (WM) fiber-tracking and microstructural characterization of axonal/neuronal integrity and connectivity. By measuring water molecules motion in the three directions of space, numerous parametric maps can be reconstructed. Among these, fractional anisotropy (FA), mean diffusivity (MD), and axial (λa) and radial (λr) diffusivities have extensively been used to investigate brain diseases. Overall, these findings demonstrated that WM and grey matter (GM) tissues are subjected to numerous microstructural alterations in multiple sclerosis (MS). However, it remains unclear whether these tissue alterations result from global processes, such as inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. Furthermore, these pathological events may occur along afferent or afferent WM fiber pathways, leading to antero- or retrograde degeneration. Thus, for a better understanding of MS pathological processes like its spatial and ...

Stamile, Claudio — Université Claude Bernard Lyon 1, KU Leuven

Tissue Characterisation from Intravascular Ultrasound using Texture Analysis

Intravascular ultrasound has, over the past decade, significantly changed the clinical diagnosis and therapeutic strategy of coronary and vascular disease assessment, as it not only allows visualisation of the vessel lumen, but gives a unique view of the pathophysiologic structure of the artery wall. This information is currently unavailable from the universally accepted instrument for artery assessment, angiography, which has on several occasions had its diagnostic accuracy questioned. With intravascular ultrasound, there is the potential to categorise diseased arterial tissue belonging to distinct pathological groups which can ultimately aid in the understanding of individual lesions as well as making a significant contribution to treatment choice and management of cardiac patients. The high resolution image information offered by intravascular ultrasound provides excellent crosssectional views of coronary artery disease at the level of the disease process itself. This information can be used ...

Nailon, William Henry — University Of Edinburgh

Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram ...

Yang, Guang — University College London

Optimal estimation of diffusion MRI parameters

Diffusion magnetic resonance imaging (dMRI) is currently the method of choice for the in vivo and non-invasive quantification of water diffusion in biological tissue. Several diffusion models have been proposed to obtain quantitative diffusion parameters, which have shown to provide novel information on the structural and organizational features of biological tissue, the brain white matter in particular. The goal of this dissertation is to improve the accuracy of the diffusion parameter estimation, given the non-Gaussian nature of the diffusion-weighted MR data. In part I of this manuscript, the necessary basics of dMRI are provided. Next, Part II deals with diffusion parameter estimation and includes the main contributions of the research. Finally, Part III covers the construction of a population-based dMRI atlas of the rat brain.

Veraart, Jelle — University of Antwerp

Natural-Scene Text Understanding

Either in color camera-based images or in low resolution thumbnails, inherent degradations, such as complex backgrounds, artistic fonts, uneven lighting or unsatisfactory resolution, must be taken into account. In order to circumvent or correct them, studies of image formation and degradation sources challengingly led to overcome too constrained definitions of color spaces. Hence the selective metric text extraction attempts to combine magnitude and directional processing of colors in an unsupervised framework. Text extraction from background is simultaneously linked to subsequent steps of character segmentation and recognition. This intermingled chain mainly aims at combining color, intensity and spatial information of pixels for robustness and accuracy. Each of these features addresses different issues; the first one for text extraction and the two latter ones for recovering initial separation between characters through log-Gabor filtering. In order to reach higher quality results, pre- and ...

Mancas-Thillou, Celine — Universite de Mons

Bayesian Fusion of Multi-band Images: A Powerful Tool for Super-resolution

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne ...

Wei, Qi — University of Toulouse

Tradeoffs and limitations in statistically based image reconstruction problems

Advanced nuclear medical imaging systems collect multiple attributes of a large number of photon events, resulting in extremely large datasets which present challenges to image reconstruction and assessment. This dissertation addresses several of these challenges. The image formation process in nuclear medical imaging can be posed as a parametric estimation problem where the image pixels are the parameters of interest. Since nuclear medical imaging applications are often ill-posed inverse problems, unbiased estimators result in very noisy, high-variance images. Typically, smoothness constraints and a priori information are used to reduce variance in medical imaging applications at the cost of biasing the estimator. For such problems, there exists an inherent tradeoff between the recovered spatial resolution of an estimator, overall bias, and its statistical variance; lower variance can only be bought at the price of decreased spatial resolution and/or increased overall bias. ...

Kragh, Tom — University of Michigan

Sparse Signal Recovery From Incomplete And Perturbed Data

Sparse signal recovery consists of algorithms that are able to recover undersampled high dimensional signals accurately. These algorithms require fewer measurements than traditional Shannon/Nyquist sampling theorem demands. Sparse signal recovery has found many applications including magnetic resonance imaging, electromagnetic inverse scattering, radar/sonar imaging, seismic data collection, sensor array processing and channel estimation. The focus of this thesis is on electromagentic inverse scattering problem and joint estimation of the frequency offset and the channel impulse response in OFDM. In the electromagnetic inverse scattering problem, the aim is to find the electromagnetic properties of unknown targets from measured scattered field. The reconstruction of closely placed point-like objects is investigated. The application of the greedy pursuit based sparse recovery methods, OMP and FTB-OMP, is proposed for increasing the reconstruction resolution. The performances of the proposed methods are compared against NESTA and MT-BCS methods. ...

Senyuva, Rifat Volkan — Bogazici University

Progressive visualization of incomplete sonar-data sets: from sea-bottom interpolation and segmentation to geometry extraction

This thesis describes a visualization pipeline for sonar profiling data that show reflections of multiple sediments in the sea bottom and that cover huge survey areas with many gaps. Visualizing such data is not trivial, because they may be noisy and because data sets may be very large. The developed techniques are: (1) Quadtree interpolation for estimating new sediment reflections, at all gaps in the longitude-latitude plane. The quadtree is used for guiding the 3D interpolation process: gaps become small at low spatial resolutions, where they can be filled by interpolating between available reflections. In the interpolation, the reflection data are cross correlated in order to construct continuity of multiple, sloping reflections. (2) Segmentation and boundary refinement in an octree in order to detect sediments in the sonar data. In the refinement, coarse boundaries are reclassified by filtering the data ...

Loke, Robert Edward — Delft University of Technology

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