| 1. |
- Cros, Olivier, 1975-
(författare)
-
Image Analysis and Visualization of the Human Mastoid Air Cell System
- 2015
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- From an engineering background, it is often believed that the human anatomy has already been fully described. Radiology has greatly contributed to understand the inside of the human body without surgical intervention. Despite great advances in clinical CT scanning, image quality is still related to a limited amount X-ray exposure for the patient safety. This limitation prevents fine anatomical structures to be visible and, more importantly, to be detected. Where such modality is of great advantage for screening patients, extracting parameters like surface area and volume implies the bone structure to be large enough in relation to the scan resolution.The mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented on a CT scan. Any statistical analysis will be biased towards air cells of smaller size. To allow a complete representation of the mastoid air cell system, a micro-CT scanner is more adequate. Micro-CT scanning uses approximately the same amount of X-rays but for a much longer exposure time compared to what is normally allowed for patients. Human temporal bone specimens are therefore necessary when using such scanning method. Where the conventional clinical CT scanner lacks level of minutes details, micro-CT scanning provides an overwhelming amount of fine details.Prior to any image analysis of medical data, visualization of the data is often needed to learn how to extract the structures of interest for further processing. Visualization of micro-CT scans is of no exception. Due to the high resolution nature of the data, visualization of such data not only requires modern and powerful computers, but also necessitates a tremendous amount of time to adjust the hiding of irrelevant structures, to find the correct orientation, while emphasising the structure of interest. Once the quality of the data has been assessed, and a strategy for the image processing has been decided, the image processing can start, to in turn extract metrics such as the surface area or volume and draw statistics from it. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scannings. The study compared current resul s with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scanning. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange.Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis was performed are described. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis, also reported in this thesis, suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.
|
|
| 2. |
- Cros, Olivier
(författare)
-
Structural properties of the mastoid using image analysis and visualization
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented in a CT scan. Where the conventional clinical CT scanner lacks level of minute details, micro-CT scanning provides an overwhelming amount of ne details. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this boneawakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scans. The study compared current results with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scanning. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange.Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative pre-analysis were performed and reported. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis suggest these microchannels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.The knowledge gained from analysing the micro-channels as locally providing blood to the mucosa, led to the consideration of how inflammation of the mucosa could impact the pneumatization of the mastoid air cell system. Though very primitive, a 3D shape analysis of the mastoid air cell system was carried out. The mastoid air cell system was first represented in a compact form through a medial axis, from which medial balls could be used. The medial balls, representative of how large the mastoid air cells can be locally, were used in two complementary clustering methods, one based on the size diameter of the medial balls and one based on their location within the mastoid air cell system. From both quantitative and qualitative statistics, it was possible to map the clusters based on pre-defined regions already described in the literature, which opened the door for new hypotheses concerning the effect of mucosal inflammation on the mastoid pneumatization.Last but not least, discovery of other structures, previously unreported in the literature, were also visually observed and briefly discussed in this thesis. Further analysis of these unknown structures is needed.
|
|
| 3. |
- Borga, Magnus, 1965-
(författare)
-
Learning Multidimensional Signal Processing
- 1998
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense.Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion.In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting.A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented.A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods.Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.
|
|
| 4. |
- Eklund, Anders, 1981-
(författare)
-
Computational Medical Image Analysis : With a Focus on Real-Time fMRI and Non-Parametric Statistics
- 2012
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.
|
|
| 5. |
- Forsberg, Daniel
(författare)
-
Robust Image Registration for Improved Clinical Efficiency : Using Local Structure Analysis and Model-Based Processing
- 2013
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration.This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon. In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation.The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.
|
|
| 6. |
- Landelius, Tomas
(författare)
-
Reinforcement Learning and Distributed Local Model Synthesis
- 1997
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Reinforcement learning is a general and powerful way to formulate complex learning problems and acquire good system behaviour. The goal of a reinforcement learning system is to maximize a long term sum of instantaneous rewards provided by a teacher. In its extremum form, reinforcement learning only requires that the teacher can provide a measure of success. This formulation does not require a training set with correct responses, and allows the system to become better than its teacher.In reinforcement learning much of the burden is moved from the teacher to the training algorithm. The exact and general algorithms that exist for these problems are based on dynamic programming (DP), and have a computational complexity that grows exponentially with the dimensionality of the state space. These algorithms can only be applied to real world problems if an efficient encoding of the state space can be found.To cope with these problems, heuristic algorithms and function approximation need to be incorporated. In this thesis it is argued that local models have the potential to help solving problems in high-dimensional spaces and that global models have not. This is motivated with the biasvariance dilemma, which is resolved with the assumption that the system is constrained to live on a low-dimensional manifold in the space of inputs and outputs. This observation leads to the introduction of bias in terms of continuity and locality.A linear approximation of the system dynamics and a quadratic function describing the long term reward are suggested to constitute a suitable local model. For problems involving one such model, i.e. linear quadratic regulation problems, novel convergence proofs for heuristic DP algorithms are presented. This is one of few available convergence proofs for reinforcement learning in continuous state spaces.Reinforcement learning is closely related to optimal control, where local models are commonly used. Relations to present methods are investigated, e.g. adaptive control, gain scheduling, fuzzy control, and jump linear systems. Ideas from these areas are compiled in a synergistic way to produce a new algorithm for heuristic dynamic programming where function parameters and locality, expressed as model applicability, are learned on-line. Both top-down and bottom-up versions are presented.The emerging local models and their applicability need to be memorized by the learning system. The binary tree is put forward as a suitable data structure for on-line storage and retrieval of these functions.
|
|
| 7. |
- Sjölund, Jens, 1987-
(författare)
-
MRI based radiotherapy planning and pulse sequence optimization
- 2015
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential.Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs.Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.
|
|
| 8. |
- Zikrin, Spartak, 1987-
(författare)
-
Large-Scale Optimization Methods with Application to Design of Filter Networks
- 2014
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Nowadays, large-scale optimization problems are among those most challenging. Any progress in developing methods for large-scale optimization results in solving important applied problems more effectively. Limited memory methods and trust-region methods represent two ecient approaches used for solving unconstrained optimization problems. A straightforward combination of them deteriorates the efficiency of the former approach, especially in the case of large-scale problems. For this reason, the limited memory methods are usually combined with a line search. We develop new limited memory trust-region algorithms for large-scale unconstrained optimization. They are competitive with the traditional limited memory line-search algorithms.In this thesis, we consider applied optimization problems originating from the design of lter networks. Filter networks represent an ecient tool in medical image processing. It is based on replacing a set of dense multidimensional lters by a network of smaller sparse lters called sub-filters. This allows for improving image processing time, while maintaining image quality and the robustness of image processing.Design of lter networks is a nontrivial procedure that involves three steps: 1) choosing the network structure, 2) choosing the sparsity pattern of each sub-filter and 3) optimizing the nonzero coecient values. So far, steps 1 and 2 were mainly based on the individual expertise of network designers and their intuition. Given a sparsity pattern, the choice of the coecients at stage 3 is related to solving a weighted nonlinear least-squares problem. Even in the case of sequentially connected lters, the resulting problem is of a multilinear least-squares (MLLS) type, which is a non-convex large-scale optimization problem. This is a very dicult global optimization problem that may have a large number of local minima, and each of them is singular and non-isolated. It is characterized by a large number of decision variables, especially for 3D and 4D lters.We develop an effective global optimization approach to solving the MLLS problem that reduces signicantly the computational time. Furthermore, we develop efficient methods for optimizing sparsity of individual sub-filters in lter networks of a more general structure. This approach offers practitioners a means of nding a proper trade-o between the image processing quality and time. It allows also for improving the network structure, which makes automated some stages of designing lter networks.
|
|
| 9. |
- Brun, Anders, 1976-
(författare)
-
Manifolds in Image Science and Visualization
- 2007
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. Different from Euclidean space, the angle sum of geodesic triangles on the sphere is always larger than 180 degrees.Signals and data found in applied research are sometimes naturally described by such curved spaces. This dissertation presents basic research and tools for the analysis, processing and visualization of such manifold-valued data, with a particular emphasis on future applications in medical imaging and visualization.Two-dimensional manifolds, i.e. surfaces, enter naturally into the geometric modelling of anatomical entities, such as the human brain cortex and the colon. In advanced algorithms for processing of images obtained from computed tomography (CT) and ultrasound imaging (US), images themselves and derived local structure tensor fields may be interpreted as two- or three-dimensional manifolds. In diffusion tensor magnetic resonance imaging (DT-MRI), the natural description of diffusion in the human body is a second-order tensor field, which can be related to the metric of a manifold. A final example is the analysis of shape variations of anatomical entities, e.g. the lateral ventricles in the brain, within a population by describing the set of all possible shapes as a manifold.Work presented in this dissertation include: Probabilistic interpretation of intrinsic and extrinsic means in manifolds. A Bayesian approach to filtering of vector data, removing noise from sampled manifolds and signals. Principles for the storage of tensor field data and learning a natural metric for empirical data.The main contribution is a novel class of algorithms called LogMaps, for the numerical estimation of logp (x) from empirical data sampled from a low-dimensional manifold or geometric model embedded in Euclidean space. The logp (x) function has been used extensively in the literature for processing data in manifolds, including applications in medical imaging such as shape analysis. However, previous approaches have been limited to manifolds where closed form expressions of logp (x) have been known. The introduction of the LogMap framework allows for a generalization of the previous methods. The application of LogMaps to texture mapping, tensor field visualization, medial locus estimation and exploratory data analysis is also presented.
|
|
| 10. |
- Eklund, Anders
(författare)
-
Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces
- 2010
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.
|
|
