A personalized cardiovascular lumped parameter model of the left-sided heart and thesystemic circulation has been developed by the cardiovascular medicine research groupat Linköping University. It provides information about hemodynamics, some of whichcould otherwise only have been retrieved by invasive measurements. The framework forpersonalizing the model is made using 4D Flow MRI data, containing volumes describinganatomy and velocities in three directions. Thus far, the inputs to this model have beengenerated manually for each subject. This is a slow and tedious process, unpractical touse clinically, and unfeasible for many subjects.This project aims to develop a tool to calculate the inputs and run the model for mul-tiple subjects in an automatic way. It has its basis in 4D Flow MRI data sets segmentedto identify the locations of left atrium (LA), left ventricle (LV), and aorta, along with thecorresponding structures on the right side.The process of making this tool started by calculation of the inputs. Planes were placedin the relevant positions, at the mitral valve, aortic valve (AV) and in the ascending aortaupstream the brachiocephalic branches, and flow rates were calculated through them. TheAV plane was used to calculate effective orifice area of AV and aortic cross-sectional area,while the LV end systolic and end diastolic volumes were extracted form the segmentation.The tool was evaluated by comparison with manually created inputs and outputs,using 9 healthy volunteers and one patient deemed to have normal left ventricular func-tion. The patient was chosen from a subject group diagnosed with chronic ischemic heartdisease, and/or a history of angina, together with fulfillment of the high risk score ofcardiovascular diseases of the European Society of Cardiology. This data was evaluatedusing coefficient of variation, Bland-Altman plots and sum squared error. The tool wasalso evaluated visually on some subjects with pathologies of interest.This project shows that it is possible to calculate inputs fully automatically fromsegmented 4D Flow MRI and run the cardiovascular avatar in an automatic way, withoutuser interaction. The method developed seems to be in good to moderate agreement withthose obtained manually, and could be the basis for further development of the model.

Medical images have been used increasingly for diagnosis, treatment planning, monitoring disease processes, and other medical applications. A large variety of medical imaging modalities exists including CT, X-ray, MRI, Ultrasound, etc. Frequently a group of images need to be compared to one another and/or combined for research or cumulative purposes. In many medical studies, multiple images are acquired from subjects at different times or with different imaging modalities. Misalignment inevitably occurs, causing anatomical and/or functional feature shifts within the images. Computerized image registration (alignment) approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. This dissertation focuses on providing automatic image registration strategies. After a through review of existing image registration techniques, we identified two registration strategies that enhance the current field: (1) an automated rigid body and affine registration using voxel similarity measurements based on a sequential hybrid genetic algorithm, and (2) an automated deformable registration approach based upon a linear elastic finite element formulation. Both methods streamlined the registration process. They are completely automatic and require no user intervention. The proposed registration strategies were evaluated with numerous 2D and 3D MR images with a variety of tissue structures, orientations and dimensions. Multiple registration pathways were provided with guidelines for their applications. The sequential genetic algorithm mimics the pathway of an expert manually doing registration. Experiments demonstrated that the sequential genetic algorithm registration provides high alignment accuracy and is reliable for brain tissues. It avoids local minima/maxima traps of conventional optimization techniques, and does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. The elastic model was shown to be highly effective to accurately align areas of interest that are automatically extracted from the images, such as brains. Using a finite element method to get the displacement of each element node by applying a boundary mapping, this method provides an accurate image registration with excellent boundary alignment of each pair of slices and consequently align the entire volume automatically. This dissertation presented numerous volume alignments. Surface geometries were created directly from the aligned segmented images using the Multiple Material Marching Cubes algorithm. Using the proposed registration strategies, multiple subjects were aligned to a standard MRI reference, which is aligned to a segmented reference atlas. Consequently, multiple subjects are aligned to the segmented atlas and a full fMRI analysis is possible.

Various hand-crafted features have been explored for medical image classification, which include SIFT and Local Binary Patterns (LBP). However, hand-crafted features may not be optimally discriminative for classifying images from particular domains (e.g. colonoscopy), as not necessarily tuned to the domain’s characteristics. In this work, I give emphasis on learning highly discriminative local features and image representations to achieve the best possible classification performance for medical images, particularly for colonoscopy and histology (cell) images. I propose approaches to learn local features using unsupervised and weakly-supervised methods, and an approach to improve the feature encoding methods such as bag-of-words. Unlike the existing work, the proposed weakly-supervised approach uses image-level labels to learn the local features. Requiring image-labels instead of region-level labels makes annotations less expensive, and closer to the data normally available from normal clinical practice, hence more feasible in practice. In this thesis, first, I propose a generalised version of the LBP descriptor called the Generalised Local Ternary Patterns (gLTP), which is inspired by the success of LBP and its variants for colonoscopy image classification. gLTP is robust to both noise and illumination changes, and I demonstrate its competitive performance compared to the best performing LBP-based descriptors on two different datasets (colonoscopy and histology). However LBP-based descriptors (including gLTP) lose information due to the binarisation step involved in their construction. Therefore, I then propose a descriptor called the Extended Multi-Resolution Local Patterns (xMRLP), which is real-valued and reduces information loss. I propose unsupervised and weakly-supervised learning approaches to learn the set of parameters in xMRLP. I show that the learned descriptors give competitive or better performance compared to other descriptors such as root-SIFT and Random Projections. Finally, I propose an approach to improve feature encoding methods. The approach captures inter-cluster features, providing context information in the feature as well as in the image spaces, in addition to the intra-cluster features often captured by conventional feature encoding approaches. The proposed approaches have been evaluated on three datasets, 2-class colonoscopy (2, 100 images), 3-class colonoscopy (2, 800 images) and histology (public dataset, containing 13, 596 images). Some experiments on radiology images (IRMA dataset, public) also were given. I show state-of-the-art or superior classification performance on colonoscopy and histology datasets.

Knee osteoarthritis is one of the most debilitating aging diseases as it causes loss of cartilage of the knee joint. Knee osteoarthritis affects the quality of life and increases the burden on health care costs. With no disease-modifying osteoarthritis drug currently available there is an immediate need to understand the factors triggering the onset and progression of the disease. Developing robust segmentation techniques and quantitative analysis helps identify potential imaging-based biomarkers that indicate the onset and progression of osteoarthritis. This thesis work developed layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework based knee MRI segmentation algorithms in 3D and longitudinal 3D (4D). A hierarchical random forest classifier algorithm was developed to improve cartilage costs functions for the LOGISMOS framework. The new cost function design significantly improved the segmentation accuracy over the existing state of the art methods. Disease progression results in more artifacts appearing similar to cartilage in MRI. 4D LOGISMOS segmentation was developed to simultaneously segment multiple time-points of a single patient by incorporating information from earlier time points with a relatively healthier knee in the early stage of the disease. Our experiments showed consistently higher segmentation accuracy across all the time-points over 3D LOGISMOS segmentation of each time-point. Fully automated segmentation algorithms proposed are not 100% accurate especially for patient MRI's having severe osteoarthritis and require interactive correction. An interactive technique called just-enough interaction (JEI) was developed which added a fast correction step to the automated LOGISMOS, speeding up the interactions substantially over the current slice-by-slice manual editing while maintaining high accuracy. JEI editing modifies the graph nodes instead of the boundary surfaces of the bones and cartilages providing globally optimally corrected results. 3D JEI was extended to 4D JEI allowing for simultaneous visualization and interaction of multiple time points of the same patients. Further quantitative analysis tools were developed to study the thickness losses. Nomenclature compliant sub-plate detection algorithm was developed to quantify thickness in the smaller load bearing regions of the knee to help understand the varying rates of thickness losses in the different regions. Regression models were developed to predict the thickness accuracy on a patient MRI at a later follow-up using the available thickness information from the LOGISMOS segmentation of the current set of MRI scans of the patient. Further non-cartilage based imaging biomarker quantification was developed to analyze bone shape changes between progressing and non-progressing osteoarthritic populations. The algorithm quantified statistically significant local shape changes between the two populations. Overall this work improved the state of the art in the segmentation of the bones and cartilage of the femur and tibia. Interactive 3D and 4D JEI were developed allowing for fast corrections of the segmentations and thus significantly improving the accuracy while performing many times faster. Further, the quantitative analysis tools developed robustly analyzed the segmentation providing measurable metrics of osteoarthritis progression.

<p>Image segmentation is the problem of partitioning an image into meaningful parts, often consisting of an object and background. As an important part of many imaging applications, e.g. face recognition, tracking of moving cars and people etc, it is of general interest to design robust and fast segmentation algorithms. However, it is well accepted that there is no general method for solving all segmentation problems. Instead, the algorithms have to be highly adapted to the application in order to achieve good performance. In this thesis, we will study segmentation methods for blood vessels in medical images. The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Common modalities such as CT and MRI generate images which simply cannot be examined manually, due to high resolutions and a large number of image slices. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation.</p><p>We have divided the segmentation of blood vessels into two parts. First, we model the vessels as a collection of lines and edges (linear structures) and use filtering techniques to detect such structures in an image. Second, the output from this filtering is used as input for segmentation tools. Our contributions mainly lie in the design of a multi-scale filtering and integration scheme for de- tecting vessels of varying widths and the modification of optimization schemes for finding better segmentations than traditional methods do. We validate our ideas on synthetical images mimicking typical blood vessel structures, and show proof-of-concept results on real medical images.</p>

<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.</p><p>The results of this work are;</p><p>1. a parameter setting tool that makes the initial parameter setting much easier and</p><p>2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.</p>

<p>Like the rest of the society modern health care has to deal with the ever increasing information flow. Imaging modalities such as CT, MRI, US, SPECT and PET just keep producing more and more data. Especially CT and MRI and their 3D image stacks cause problems in terms of how to effectively handle these data sets. Usually a PACS is used to manage the information flow. Since a PACS often is implemented with a server-client setup, the management of these large data sets requires an efficient representation of medical image stacks that minimizes the amount of data transmitted between server and client and that efficiently supports the workflow of a practitioner.</p><p>In this thesis an efficient wavelet representation for large medical image stacks is proposed for the use in a PACS. The representation supports features such as lossless viewing, random access, ROI-viewing, scalable resolution, thick slab viewing and progressive transmission. All of these features are believed to be essential to form an efficient tool for navigation and reconstruction of an image stack.</p><p>The proposed wavelet representation has also been implemented and found to be better in terms of memory allocation and amount of data transmitted between server and client when compared to prior solutions. Performance tests of the implementation has also shown the proposed wavelet representation to have a good computational performance.</p>

Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension.

Modern medical imaging techniques provide an increasing amount of high-dimensional and high-resolution image data that need to be visualized, analyzed, and interpreted for diagnostic and treatment planning purposes. As a consequence, efficient ways of exploring these images are needed. In order to work with specific patient cases, it is necessary to be able to work directly with the medical image volumes and to generate the relevant 3D structures directly as they are needed for visualization and analysis. This requires efficient tools for segmentation, i.e., separation of objects from each other and from the background. Segmentation is hard to automate due to, e.g., high shape variability of organs and limited contrast between tissues. Manual segmentation, on the other hand, is tedious and error-prone. An approach combining the merits from automatic and manual methods is semi-automatic segmentation, where the user interactively provides input to the methods. For complex medical image volumes, the interactive part can be highly 3D oriented and is therefore dependent on the user interface. This thesis presents methods for interactive segmentation and visualization where true 3D interaction with haptic feedback and stereo graphics is used. Well-known segmentation methods such as fast marching, fuzzy connectedness, live-wire, and deformable models, have been tailored and extended for implementation in a 3D environment where volume visualization and haptics are used to guide the user. The visualization is accelerated with graphics hardware and therefore allows for volume rendering in stereo at interactive rates. The haptic feedback is rendered with constraint-based direct volume haptics in order to convey information about the data that is hard to visualize and thereby facilitate the interaction. The methods have been applied to real medical images, e.g., 3D liver CT data and 4D breast MR data with good results. To provide a tool for future work in this area, a software toolkit containing the implementations of the developed methods has been made publicly available.

Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems. The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells. A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender. A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification. An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.