Image Features for Tuberculosis Classification in Digital Chest Radiographs

Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research

Characterization of Bone Material Properties and Microstructure in Osteogenesis Imperfecta/Brittle Bone Disease

Osteogenesis imperfecta (OI) is a genetic disorder primarily associated with mutations to type I collagen and resulting in mild to severe bone fragility. To date, there is very little data quantifying OI cortical bone mechanics. The purpose of this dissertation was to investigate bone microstructure, mineralization, and mechanical properties in adolescents with OI. Characterization studies were performed on small osteotomy specimens obtained from the extremities during routine corrective surgeries. Nanoindentation was used to examine the longitudinal elastic modulus and hardness at the material level for mild OI type I vs. severe OI type III. Both modulus and hardness were significantly higher (by 7% and 8%, respectively) in mild OI cortical bone compared to the more severe phenotype. Lamellar microstructure also affected these properties, as the younger bone material immediately surrounding osteons showed decreased modulus (13%) and hardness (11%) compared to the older interstitial material. A high resolution micro-computed tomography system utilizing synchrotron radiation (SRµCT) was described and used to analyze the microscale vascular porosity, osteocyte lacunar morphometry, and bone mineral density in OI vs. healthy individuals. Vascular porosity, canal diameter, and osteocyte lacunar density were all two to six times higher in OI cortical bone. Osteocytes were also more spherical in shape. Finally, three-point bending techniques were used to evaluate the microscale mechanical properties of OI cortical bone in two different orientations. Elastic modulus, flexural yield strength, ultimate strength, and crack-growth toughness were three to six times higher in specimens whose pore structure was primarily oriented parallel vs. perpendicular to the long bone axis. There was also a strong negative correlation between the elevated vascular porosity of OI cortical bone and its elastic modulus, flexural yield strength, and ultimate strength. This relationship was independent of osteocyte lacunar density and tissue mineral density. In summary, these findings highlight new material and microstructural changes within OI cortical bone that help contribute to its fragility. They also underscore a deep connection between bone structure and mechanical integrity at multiple length scales

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Bodies of Seeing: A video ethnography of academic x-ray image interpretation training and professional vision in undergraduate radiology and radiography education

This thesis reports on a UK-based video ethnography of academic x-ray image interpretation training across two undergraduate courses in radiology and radiography. By studying the teaching and learning practices of the classroom, I initially explore the professional vision of x-ray image interpretation and how its relation to normal radiographic anatomy founds the practice of being ‘critical’. This criticality accomplishes a faculty of perceptual norms that is coded and organised and also, therefore, of a specific radiological vision. Professionals’ commitment to the cognitivist rhetoric of ‘looking at’/‘pattern recognition’ builds this critical perception, a perception that deepens in organisation when professionals endorse a ‘systematic approach’ that mediates matter-of-fact thoroughness and offers a helpful critical commentary towards the image. In what follows, I explore how x-ray image interpretation is constituted in case presentations. During training, x-ray images are treated with suspicion and as misleading and are aligned with a commitment to discursive contexts of ‘missed abnormality’, ‘interpretive risk’, and ‘technical error’. The image is subsequently constructed as ambiguous and that what is shown cannot be taken at face value. This interconnects with reenacting ideals around ‘seeing clearly’ that are explained through the teaching practices and material world of the academic setting and how, if misinterpretation is established, the ambiguity of the image is reduced by embodied gestures and technoscientific knowledge. By making this correction, the ambiguous image is reenacted and the misinterpretation of image content is explained. To conclude, I highlight how the professional vision of academic x-ray image interpretation prepares students for the workplace, shapes the classificatory interpretation of ab(normal) anatomy, manages ambiguity through embodied expectations and bodily norms, and cultivates body-machine relations