Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises

Fast Computation of Hahn Polynomials for High Order Moments

Discrete Hahn polynomials (DHPs) and their moments are considered to be one of the efficient orthogonal moments and they are applied in various scientific areas such as image processing and feature extraction. Commonly, DHPs are used as object representation; however, they suffer from the problem of numerical instability when the moment order becomes large. In this paper, an operative method to compute the Hahn orthogonal basis is proposed and applied to high orders. This paper developed a new mathematical model for computing the initial value of the DHP and for different values of DHP parameters (α and β). In addition, the proposed method is composed of two recurrence algorithms with an adaptive threshold to stabilize the generation of the DHP coefficients. It is compared with state-of-the-art algorithms in terms of computational cost and the maximum size that can be correctly generated. The experimental results show that the proposed algorithm performs better in both parameters for wide ranges of parameter values α and β, and polynomial sizes

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

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Quantitative Analysis of Three-Dimensional Cone-Beam Computed Tomography Using Image Quality Phantoms

In the clinical setting, weight-bearing static 2D radiographic imaging and supine 3D radiographic imaging modalities are used to evaluate radiographic changes such as, joint space narrowing, subchondral sclerosis, and osteophyte formation. These respective imaging modalities cannot distinguish between tissues with similar densities (2D imaging), and do not accurately represent functional joint loading (supine 3D imaging). Recent advances in cone-beam CT (CBCT) have allowed for scanner designs that can obtain weight-bearing 3D volumetric scans. The purpose of this thesis was to analyze, design, and implement advanced imaging techniques to quantify image quality parameters of reconstructed image volumes generated by a commercially-available CBCT scanner, and a novel ceiling-mounted CBCT scanner. In addition, imperfections during rotation of the novel ceiling-mounted CBCT scanner were characterized using a 3D printed calibration object with a modification to the single marker bead method, and prospective geometric calibration matrices

Development of radiofrequency pulses for fast and motion-robust brain MRI

This thesis is based on three projects and the three scientific articles that were the result of each project. Each project deals with various kinds of technical software development in the field of magnetic resonance imaging (MRI). The projects are in many ways very different, encompassing several acquisition and reconstruction strategies. However, there are at least two common denominators. The first is the projects shared the same goal of producing fast and motion robust methods. The second common denominator is that all the projects were carried out with a particular focus on the radiofrequency (RF) pulses used. The first project combined the acceleration method simultaneous multi-slice (SMS) with the acquisition method called PROPELLER. This combination was utilized to acquire motion-corrected thin-sliced reformattable T2-weighted and T1-FLAIR image volumes, thereby producing a motion robust alternative to 3D sequences. The second project analyzed the effect of the excitation RF pulse on T1-weighted images acquired with 3D echo planar imaging (EPI). It turned out that an RF pulse that reduced magnetization transfer (MT) effects significantly increased the gray/white matter contrast. The 3D EPI sequence was then used to rapidly image tumor patients after gadolinium enhancement. The third project combined PROPELLER’s retrospective motion correction with the prospective motion correction of an intelligent marker (the WRAD). With this combination, sharp T1-FLAIR images were acquired during large continuous head movements

Investigation of Neonatal Pulmonary Structure and Function via Proton and Hyperpolarized Gas Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a modality that utilizes the phenomenon of nuclear magnetic resonance (NMR) to yield tomographic images of the body. Proton (1H) MRI has historically been successful in soft tissues but has suffered in the lung due to a variety of technical challenges, such as the low proton-density, rapid T2* relaxation time of the lung parenchymal tissue, and inherent physiological motion in the chest. Recent developments in radial ultrashort echo time (UTE) MRI have in part overcome these issues. In addition, there has been much progress in techniques for hyperpolarization of noble gases (3He and 129Xe) out of thermal equilibrium via spin exchange optical pumping, which can greatly enhance the gas NMR signal such that it is detectable within the airspaces of the lung on MRI. The lung is a unique organ due to its complex structural and functional dynamics, and its early development through the neonatal (newborn) period is not yet well understood in normal or abnormal conditions. Pulmonary morbidities are relatively common in infants and are present in a majority of patients admitted to the neonatal intensive care unit, often stemming from preterm birth and/or congenital defects. Current clinical lung imaging in these patients is typically limited to chest x-ray radiography, which does not provide tomographic information and so has lowered sensitivity. More rarely, x-ray computed tomography (CT) is used but exposes infants to ionizing radiation and typically requires sedation, both of which pose increased risks to pediatric patients. Thus the opportunity is ripe for application of novel pulmonary MRI techniques to the infant population. However, MR imaging of very small pulmonary structure and microstructure requires fundamental changes in the imaging theory of both 1H UTE MRI and hyperpolarized gas diffusion MRI. Furthermore, such young patients are often non-compliant, yielding a need for new and innovative techniques for monitoring respiratory and bulk motion. This dissertation describes methodology development and provides experimental results in both 1H UTE MRI and hyperpolarized 3He and 129Xe gas diffusion MRI, with investigation into the structure and function of infant lungs at both the macrostructural and microstructural level. In particular, anisotropically restricted gas diffusion within infant alveolar microstructure is investigated as a measurement of airspace size and geometry. Additionally, the phenomenon of respiratory and bulk motion-tracking via modulation of the k-space center\u27s magnitude and phase is explored and applied via UTE MRI in various neonatal pulmonary conditions to extract imaging-based metrics of diagnostic value. Further, the proton-density regime of pulmonary UTE MRI is validated in translational applications. These techniques are applied in infants with various pulmonary conditions, including patients diagnosed with bronchopulmonary dysplasia, congenital diaphragmatic hernia, esophageal atresia/tracheoesophageal fistula, tracheomalacia, and no suspected lung disease. In addition, explanted lung specimens from both infants with and without lung disease are examined. Development and implementation of these techniques involves a strong understanding of the physics-based theory of NMR, hyperpolarization, and MR imaging, in addition to foundations in hardware, software, and image analysis techniques. This thesis first outlines the theory and background of NMR, MRI, and pulmonary physiology and development (Part I), then proceeds into the theory, equipment, and imaging experiments for hyperpolarized gas diffusion MRI in infant lung airspaces (Part II), and finally details the theory, data processing methods, and applications of pulmonary UTE MRI in infant patients (Part III). The potential for clinical translation of the neonatal pulmonary MRI methods presented in this dissertation is very high, with the foundations of these techniques firmly rooted in the laws of physics

Advancing Magnetic Resonance Spectroscopy and Endoscopy with Prior Knowledge

Reconstruction is key to the generation of anatomic, functional and biochemical information in the field of Magnetic Resonance (MR) in medicine. Here, prior knowledge based on various conditions is utilized through reconstruction to accelerate current MR techniques and reduce artifacts. First, prior knowledge from Magnetic Resonance Imaging (MRI) is exploited to accelerate spatial localization in Magnetic Resonance Spectroscopy (MRS). The MRS information is contained in one extra chemical shift dimension, beyond the three spatial dimensions of MRI, and can provide valuable in vivo metabolic information for the study of numerous diseases. However, its research and clinical applications are often compromised by long scan times. Here, a new method of localized Spectroscopy with Linear Algebraic Modeling (SLAM) is proposed for accelerating MRS scans. The method assumes pre-conditions that the MRS scan is preceded by a scout MRI scan and that a compartment-averaged MRS measurement will suffice for the assessment of metabolic status. SLAM builds a priori MRI-based segmentation information into the standard Fourier-encoded MRS model of chemical shift imaging (CSI), to directly reconstruct compartmental spectra. Second, SLAM is extended to higher dimensions and to incorporate parallel imaging techniques that deploy pre-acquired sensitivity information based on the use of separate multiple receive-coil elements, to further accelerate scan speed. In addition, eddy current-induced phase effects are incorporated into the SLAM model, and a modified reconstruction algorithm provides improved suppression of signal leakage due to heterogeneity in the MRS signal, especially when employing sensitivity encoding. Third, prior information from MRI is also used to reduce the problem of lipid artifacts in 1H brain CSI. CSI is routinely used for human brain MRS studies, and low spatial resolution in CSI causes partial volume error and signal ‘bleed’ that is especially deleterious to voxels near the scalp. A standard solution is to apply spatial apodization, which adversely affects spatial resolution. Here, a novel automated strategy for partial volume correction that employs grid shifting (‘PANGS’) is presented, which minimizes lipid signal bleed without compromising spatial resolution. PANGS shifts the reconstruction coordinate in a designated region of image space—the scalp, identified by MRI—to match the tissue center of mass instead of the geometric center of each voxel. Last, prior knowledge of the spatially sparse nature of endoscopic MRI images acquired with tiny internal MRI antennae, and that of the null signal location of the endoscopic probe, are used to accelerate MR endoscopy and reduce motion artifacts. High-resolution endoscopic MRI is susceptible to degradation from physiological motion, which can necessitate time-consuming cardiac gating techniques. Here, we develop acceleration techniques based on the compressed sensing theory, and un-gated motion compensation strategies using projection shifting, to effectively produce faster motion-suppressed MRI endoscopy