Interactive Segmentation of 3D Medical Images with Implicit Surfaces

To cope with a variety of clinical applications, research in medical image processing has led to a large spectrum of segmentation techniques that extract anatomical structures from volumetric data acquired with 3D imaging modalities. Despite continuing advances in mathematical models for automatic segmentation, many medical practitioners still rely on 2D manual delineation, due to the lack of intuitive semi-automatic tools in 3D. In this thesis, we propose a methodology and associated numerical schemes enabling the development of 3D image segmentation tools that are reliable, fast and interactive. These properties are key factors for clinical acceptance. Our approach derives from the framework of variational methods: segmentation is obtained by solving an optimization problem that translates the expected properties of target objects in mathematical terms. Such variational methods involve three essential components that constitute our main research axes: an objective criterion, a shape representation and an optional set of constraints. As objective criterion, we propose a unified formulation that extends existing homogeneity measures in order to model the spatial variations of statistical properties that are frequently encountered in medical images, without compromising efficiency. Within this formulation, we explore several shape representations based on implicit surfaces with the objective to cover a broad range of typical anatomical structures. Firstly, to model tubular shapes in vascular imaging, we introduce convolution surfaces in the variational context of image segmentation. Secondly, compact shapes such as lesions are described with a new representation that generalizes Radial Basis Functions with non-Euclidean distances, which enables the design of basis functions that naturally align with salient image features. Finally, we estimate geometric non-rigid deformations of prior templates to recover structures that have a predictable shape such as whole organs. Interactivity is ensured by restricting admissible solutions with additional constraints. Translating user input into constraints on the sign of the implicit representation at prescribed points in the image leads us to consider inequality-constrained optimization

7th International Meshing Roundtable '98

The goal of the 7th International Meshing Roundtable is to bring together researchers and developers from industry, academia, and government labs in a stimulating, open environment for the exchange of technical information related to the meshing process. In the past, the Roundtable has enjoyed significant participation from each of these groups from a wide variety of countries

Computational analysis and design of the morphology of organic solar cells

Projected increases in energy demand and increasing global concern regarding climate change have focused research attention on renewable, clean energy sources, such as organic solar cells (OSCs). OSCs based on blend of electron donating conjugate polymers and electron accepting fullerene materials have potential to be a disruptive technology within the solar cell market. OSCs have several advantages over their inorganic counterparts. The materials used in most OSCs are flexible, allowing for easier transport and installation of such devices. However, because of OSCs main deficiency of significantly low photocurrent generation, they have not yet seen mass adoption in industry. The problem of low photocurrent generation of OSCs is examined in this thesis. In this thesis we focus on computational study and design of OSCs. We explore various strategies in order to increase photocurrent generation of OSCs through morphology improvement. We perform OSC simulation using both continuum and atomistic models. Our continuum model is based on excitonic drift-diffusion (XDD) equations. First, we develop an efficient and fast framework for simulation of OSC current generation. Second, we apply the morphology aware XDD model to the experimentally retrieved large domain morphology for the first time with an in-house parallel computing framework using the finite element method. Third, we couple, XDD model with CAD and optimization techniques to design new 2D and 3D microstructures whose performance is better than known microstructures (bilayer and bulk heterojunctions). Later we develop an atomistic model in order to investigate effect of different parameters on the performance of the organic solar cell. we utilize a directed graph representation to relate the microscopic properties of polymer systems and the consequent charge transfer

Pattern recognition techniques applied to rust classification in steel structures

The life and performance of steel structure depends directly upon the steel surface preparation. The restoration of steel structure such as steel bridges, ships and storage tanks is due mainly to the use of manual surface inspection methods accompanied by surface preparation technologies. It requires a long project duration, high costs and hazardous practices for both worker and environment to complete surface restoration. The developments of surface preparation technologies make it essential to develop technologies that allows patch restore of corrode steel structure in practice. This thesis addresses the problem of classification of rust steel surfaces. Various Pattern recognition methods are studied for classifying less subjective steel surfaces from a time corrosion perspective. Our primary contribution is: with appropriate features from the steel surfaces, artificial neural network pattern recognition methods have the abilities to classify the less subjective rust steel surfaces reliably and be suitable for automation. The results provide important information about the classification methods for rust steel surface analysis

Inferring relevance from eye movements with wrong models

Statistical inference forms the backbone of modern science. It is often viewed as giving an objective validation for hypotheses or models. Perhaps for this reason the theory of statistical inference is often derived with the assumption that the "truth" is within the model family. However, in many real-world applications the applied statistical models are incorrect. A more appropriate probabilistic model may be computationally too complex, or the problem to be modelled may be so new that there is little prior information to be incorporated. However, in statistical theory the theoretical and practical implications of the incorrectness of the model family are to a large extent unexplored. This thesis focusses on conditional statistical inference, that is, modeling of classes of future observations given observed data, under the assumption that the model is incorrect. Conditional inference or prediction is one of the main application areas of statistical models which is still lacking a conclusive theoretical justification of Bayesian inference. The main result of the thesis is an axiomatic derivation where, given an incorrect model and assuming that the utility is conditional likelihood, a discriminative posterior yields a distribution on model parameters which best agrees with the utility. The devised discriminative posterior outperforms the classical Bayesian joint likelihood-based approach in conditional inference. Additionally, a theoretically justified expectation maximization-type algorithm is presented for obtaining conditional maximum likelihood point estimates for conditional inference tasks. The convergence of the algorithm is shown to be more stable than in earlier partly heuristic variants. The practical application field of the thesis is inference of relevance from eye movement signals in an information retrieval setup. It is shown that relevance can be predicted to some extent, and that this information can be exploited in a new kind of task, proactive information retrieval. Besides making it possible to design new kinds of engineering applications, statistical modeling of eye tracking data can also be applied in basic psychological research to make hypotheses of cognitive processes affecting eye movements, which is the second application area of the thesis

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

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention