In deformation based morphometry (DBM) group-wise differences in brain structure are measured using deformable registration and some form of statistical test. However, it is known that DBM results are sensitive to both the registration method and statistical test used. Given the lack of an objective model of group variation it has been difficult to determine the extent of the influence of registration implementation or contraints on DBM analysis. In this thesis, we use registration methods with varying levels of theoretic similarity to study the influence of registration mechanics on DBM results. We show that because of the extent of the influence of registration mechanics on DBM results, analysis of changes should always be made with a thorough understanding of the registration method used. We also show that minor variations in registration methods can lead to large changes in DBM results. When using DBM, it would be imprudent to use only one registration method to draw any conclusions about the variations being studied. In order to provide a more complete representation of inter-group changes, we propose a method for combining multiple registration methods using Dempster-Shafer evidence theory to produce belief maps of categorical changes between groups. We show that the Dempster-Shafer combination produces a unique and easy to interpret belief map of regional changes between and within groups without the complications associated with hypothesis testing. Another, often confounding, element of DBM is the parametric hypothesis test used to specify voxels undergoing significant change between the two groups. The accuracy and reliability of these tests are contingent on a number of fundamental assumptions made about the distribution of the data used in the tests. Many DBM studies often overlook these assumptions and fail to verify their validity for the data being tested. This raises many doubts about the credibility of the results from such tests. In this thesis, we propose to perform statistical analysis on DBM data using nonparametric, distribution independent hypothesis tests. With no data distributional assumptions, these tests provide both increased flexibility and reliability of DBM statistical analysis. / Ph. D.

This work consists of three original articles and is focused on the overall question: How can hyperspectral imaging contribute to patient safety and improve outcomes during abdominal surgery? The hypothesis was that HSI is suitable for the intraoperative assessment of tissue structures and decision support in routine clinical use. Spectral imaging was performed with the TIVITA Tissue for open surgery or TIVITA Mini system for laparoscopic HSI from Diaspective Vision GmbH (Am Salzhaff-Pepelow, Germany). Both HSI systems use pushbroom mode and provide 100 spectral channels in the visible and near-infrared spectral range from 500 - 1000 nm. The Number of Effective Pixels is at least 640 × 480 (x-, y-axis), while the field of view and spatial resolution depend on the measurement distance and the used focal length of the objective. Illumination is done by halogen spots for open surgery and broadband LED in the laparoscopic system. The first part of this work aimed to evaluate HSI for the measurement of ischemic conditioning effects of the gastric conduit during esophagectomy. Ischemic preconditioning by dividing major blood vessels of the stomach prior to gastric pull-up is performed to improve the perfusion at the later esophagogastric anastomosis to reduce the risk of leaks. Intraoperative hyperspectral records of the gastric tube were acquired from 22 patients through the mini-thoracotomy. Fourteen of them underwent ischemic conditioning of the stomach several days before the two-step transthoracic esophagectomy and gastric pull-up with intrathoracic anastomosis was performed. The tip of the gastric tube (later esophago-gastric anastomosis) was measured with HSI. These in vivo records showed that the tissue oxygenation of the gastric conduit was significantly higher in patients who underwent ischemic conditioning (78% vs. 66%; p = 0.03). In the second part of this work, a novel hyperspectral imaging system for MIS is described and evaluated to address the requirements for clinical use and high-resolution spectral imaging. Reference objects and resected human tissue were used to show spectral conformity with the approved HSI device for open surgery. Furthermore, varying object distances were investigated and the signal-to-noise ratio (SNR) for different light sources were measured. Measurements with both systems were performed on a human tissue resectate and compared quantitatively. It was shown that the handheld design of the laparoscopic HSI system enables the processing and visualization of spectral data in parallel during acquisition within a few seconds. The obtained measurements from both spectral imaging devices were consistent and a mean SNR of 30 to 43 dB (500 to 950 nm) was found using a standard rigid laparoscope in combination with a broadband LED light source. Finally, in the third part of this work, different image registration methods were investigated to compensate for small movements of the laparoscope and tissue deformations. The obtained image transformation is used to augment the laparoscopic color video with the static HSI data to support intraoperative localization. Multiple feature-based algorithms and a pre-trained deep homography neural network (DH-NN) were evaluated for the estimation of appropriate image transformations (single and multi-homography). The methods were validated with a ground truth dataset of 750 annotated laparoscopic images, that was created during this work, and in vivo data from the TIVITA Mini system. All feature-based single homography methods outperformed the fine-tuned DH-NN in terms of reprojection error, Structural Similarity Index Measure (SSIM), and processing time. The feature detector and descriptor ORB1000 enabled video-rate registration of laparoscopic images on standard hardware with submillimeter accuracy. Therefore, all initially stated research questions could be confirmed with the applied methods. Although technical limitations have been identified, the non-invasive and contact-free measurement principle makes HSI attractive for a variety of surgical disciplines.:1 Introduction 1.1 Interaction of light and biological tissue 1.2 Spectral imaging systems 1.3 Medical applications of spectral imaging 1.4 Intraoperative visualization of spectral data 2 Original Articles 2.1 Evaluation of hyperspectral imaging (HSI) for the measurement of ischemic conditioning effects of the gastric conduit during esophagectomy 2.2 Laparoscopic system for simultaneous high-resolution video and rapid hyperspectral imaging in the visible and near-infrared spectral range 2.3 Comparison of image registration methods for combining laparoscopic video and spectral image data 3 Summary 3.1 Conclusions and Outlook 4 References

Image registration is the process of relating the intensity values of one image to another image using their pixel c~?tent alone. An example use of this technique is to create panoramas from individual images taken froin a rotating camera. A class of image registration algorithms, known as direct registration methods, uses intensity derivatives to iteratively estimate the parameters modeling the transformation between the images. Direct methods are known for their sub-pixel accurate results; however, their execution is computationally expensive, often times preventing use in an embedded capacity like those encountered in small UIUllann~d aerial vehicle or mobile phone applications. In this work, a high performance FPGA-based direct affine image registration core is presented. The proposed method combines two features: a fully pipelined architecture to compute the linear system of equations, and a Gaussian elimination module, implemented as a finite state machine, to solve the resulting linear system. The design is implemented on a Xilinx ML506 development board featuring a Virtex-5 SX50 FPGA, zero bus turn-around (ZBT) RAM, and VGA input. Experimentation is performed on both real and synthetic data. The registration core performs in excess of 80 frames per second on 640x480 images using one registration iteration.

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Medical imaging has seen a rapid development in its clinical use in assessment of treatment outcome, disease monitoring and diagnosis over the last few decades. Yet, the vast amount of available image data limits the practical use of this potentially very valuable source of information for radiologists and physicians. Therefore, the design of computer-aided medical image analysis is of great importance to imaging in clinical practice. This thesis deals with the problem of deformable image registration in the context of lung imaging, and addresses three of the major challenges involved in this challenging application, namely: designing an image similarity for multi-modal scans or scans of locally changing contrast, modelling of complex lung motion, which includes sliding motion, and approximately globally optimal mathematical optimisation to deal with large motion of small anatomical features. The two most important contributions made in this thesis are: the formulation of a multi-dimensional structural image representation, which is independent of modality, robust to intensity distortions and very discriminative for different image features, and a discrete optimisation framework, based on an image-adaptive graph structure, which enables a very efficient optimisation of large dense displacement spaces and deals well with sliding motion. The derived methods are applied to two different clinical applications in pulmonary image analysis: motion correction for breathing-cycle computed tomography (CT) volumes, and deformable multi-modal fusion of CT and magnetic resonance imaging chest scans. The experimental validation demonstrates improved registration accuracy, a high quality of the estimated deformations, and much lower computational complexity, all compared to several state-of-the-art deformable registration techniques.

Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2010. / Contains bibliography and index. / ENGLISH ABSTRACT: Super-resolution imaging is the process whereby several low-resolution photographs of an object are combined to form a single high-resolution estimation. We investigate each component of this process: image acquisition, registration and reconstruction. A new feature detector, based on the discrete pulse transform, is developed. We show how to implement and store the transform efficiently, and how to match the features using a statistical comparison that improves upon correlation under mild geometric transformation. To simplify reconstruction, the imaging model is linearised, whereafter a polygon-based interpolation operator is introduced to model the underlying camera sensor. Finally, a large, sparse, over-determined system of linear equations is solved, using regularisation. The software developed to perform these computations is made available under an open source license, and may be used to verify the results. / AFRIKAANSE OPSOMMING: In super-resolusie beeldvorming word verskeie lae-resolusie foto's van 'n onderwerp gekombineer in 'n enkele, hoë-resolusie afskatting. Ons ondersoek elke stap van hierdie proses: beeldvorming, -belyning en hoë-resolusie samestelling. 'n Nuwe metode wat staatmaak op die diskrete pulstransform word ontwikkel om belangrike beeldkenmerke te vind. Ons wys hoe om die transform e ektief te bereken en hoe om resultate kompak te stoor. Die kenmerke word vergelyk deur middel van 'n statistiese model wat bestand is teen klein lineêre beeldvervormings. Met die oog op 'n vereenvoudigde samestellingsberekening word die beeldvormingsmodel gelineariseer. In die nuwe model word die kamerasensor gemodelleer met behulp van veelhoek-interpolasie. Uiteindelik word 'n groot, yl, oorbepaalde stelsel lineêre vergelykings opgelos met behulp van regularisering. Die sagteware wat vir hierdie berekeninge ontwikkel is, is beskikbaar onderhewig aan 'n oopbron-lisensie en kan gebruik word om die gegewe resultate te veri eer.

Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.

Buildings and public infrastructures are crucial to our societies in that they provide habitations, workplaces, commodities and services indispensible to our daily life. As vital parts of facility management, operations and maintenance (O&M) ensure a facility to continuously function as intended, which take up the longest time in a facility’s life cycle and demand great expense. Therefore, computers and information technology have been actively adopted to automate traditional maintenance methods and processes, making O&M faster and more reliable. Augmented reality (AR) offers a new approach towards human-computer interaction through directly displaying information related to real objects that people are currently perceiving. People’s sensory perceptions are enhanced (augmented) with information of interest naturally without deliberately turning to computers. Hence, AR has been proved to be able to further improve O&M task performance. The research motif of this thesis is user evaluations of AR applications in the context of facility maintenance. The studies look into invisible target designation tasks assisted by developed AR tools in both indoor and outdoor scenarios. The focus is to examine user task performance, which is influenced by both AR system performance and human perceptive, cognitive and motoric factors. Target designation tasks for facility maintenance entail a visualization-interaction dilemma. Two AR systems built upon consumer-level hand-held devices using an off-the-shelf AR software development toolkit are evaluated indoors with two disparate solutions to the dilemma – remote laser pointing and the third person perspective (TPP). In the study with remote laser pointing, the parallax effect associated with AR “X-ray vision” visualization is also an emphasis. A third hand-held AR system developed in this thesis overlays infrared information on façade video, which is evaluated outdoors. Since in an outdoor environment marker-based tracking is less desirable, an infrared/visible image registration method is developed and adopted by the system to align infrared information correctly with the façade in the video. This system relies on the TPP to overcome the aforementioned dilemma.

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To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed “blocks”, are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.