Accurate registration of 3D data is one of the most challenging problems in a number of Computer Vision applications. Visual Odometry is one such application, which determines the motion, or change in position of a moving rover by registering 3D data captured by an on-board range sensor, in a pairwise manner. The performance of Visual Odometry depends upon two main factors, the first being the quality of 3D data, which itself depends upon the type of sensor being used. The second factor is the robustness of the registration algorithm. Where sensors like stereo cameras and LIDAR scanners have been used in the past to improve the performance of Visual Odometry, the introduction of the Velodyne LIDAR scanner is fairly new and has been less investigated, particularly for odometry applications. This thesis presents and examines a new method for registering 3D point clouds generated by a Velodyne scanner mounted on a moving rover. The method is based on one of the the most widely used registration algorithms called Iterative Closest Point (ICP). The proposed method is divided into two steps. The first step, which is also the main contribution of this work, is the introduction of a new point sampling method, which prudently select points that belong to the regions of greatest geometric variance in the scan. Interest Point (Region) Sampling plays an important role in the performance of ICP by effectively discounting the regions with non-uniform resolution and selecting regions with a high geometric variance and uniform resolution. Second step is to use sampled scan pairs as the input to a new plane-to-plane variant of ICP, known as Generalized ICP. Several experiments have been executed to test the compatibility and robustness of Interest Point Sampling (IPS) for a variety of terrain landscapes. Through these experiments, which include comparisons of variants of ICP and past sampling methods, this work demonstrates that the combination of IPS and GICP results in the least localization error as compared to all other tested method. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-11-03 11:12:43.596

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In the domain of computer vision, registration is the process of calculating the transformation between a known object, called a marker, and a camera which is viewing it. Registration is the foundation for a number of applications across a range of disciplines such as augmented reality, medical imaging and robotic navigation. In the set of two dimensional planar markers, there are two classes: (1) fiducial, which are designed to be easily recognisable by computers but have little to no semantic meaning to people, and (2) natural features, which have meaning to people, but can still be registered by a computer. As computers become more powerful, natural feature markers are increasingly the more popular choice; however there are still a number of inherent problems with this class of markers. This thesis examines the most common shortcomings of natural feature markers, and proposes and evaluates solutions to these weaknesses. The work starts with a review of the existing planar registration approaches, both fiducial and natural features, with a focus on the strengths and weaknesses of each. From this review, the theory behind planar registration is discussed, from the different coordinate systems and transformations, to the computation of the registration transformation. With a foundation of planar registration, natural feature registration is decomposed into its main stages, and each stage is described in detail. This leads into a discussion of the complete natural feature registration pipeline, highlighting common issues encountered at each step, and discussing the possible solutions for each issue. A new implementation of natural feature registration called the Optical-flow Perspective Invariant Registration Augmentation (OPIRA) is proposed, which provides vast improvements in robustness to perspective, rotation and changes in scale to popular registration algorithms such as SIFT, SURF, and the Ferns classifier. OPIRA is shown to improve perspective invariance on average by 15% for SIFT, 25% for SURF and 20% for the Ferns Classifier, as well as provide complete rotation invariance for the rotation dependent implementations of these algorithms. From the investigation into problems and potential resolutions at each stage during registration, each proposed solution is evaluated empirically against an external ground truth. The results are discussed and a conclusion on the improvements gained by each proposed solution and the feasibility of use in a real natural feature registration application is drawn. Finally, some applications which use the research contained within this thesis are described, as well as some future directions for the research.

Anatomical changes can have significant clinical impact during head and neck radiotherapy. Adaptive radiotherapy (ART) may be applied to account for such changes. Implementation of ART to alter dose delivery requires deformable image registration (DIR) to assess 3D deformations. This study evaluates the performance and accuracy of a commercial DIR system for clinical applications. The investigations in this project were carried out using images of induced changes in two standard radiotherapy phantoms (RANDO® and CIRS®) and one in-house built phantom. CT image data before and after deformation of the phantoms were processed using Eclipse / SmartAdapt® v.10 system employing a Demons-based algorithm. A DIR protocol was designed, and algorithm performance was assessed quantitatively, using volume analysis and the Dice Similarity Index (DSI), and also evaluated qualitatively. In addition, algorithm performance was assessed for 5 head and neck cancer patients using clinical CT images. Each original planning CT image containing contours of 10 volumes of interest including treatment target volumes and organs at risk was deformed to match a second CT image acquired during the course of the treatment. The original structures were deformed, copied onto the target image and compared to reference contours drawn by 3 radiation oncologists. Phantom investigations gave varied results with average DSI scores ranging from 0.69 to 0.93, with an overall average of 0.86 ± 0.08. These quantitative results were reflected qualitatively, with generally accurate matching between reference and DIR-generated structures. Although air gaps in the phantoms compromised algorithm performance and gave rise to physically aberrant results. Clinical results were generally better with a DSI range of 0.75-0.99 and an overall average of 0.89 ± 0.05, suggesting high DIR accuracy. Qualitatively, some minor contour deformations were noted, as well as artefacts in the axial direction that were due to the CT slice resolution (3 mm) that was used to scan the patients. In addition, contour propagation between images using DIR reduced the time required by physicians to contour the images of head and neck cancer patients by ~47%. This study demonstrated that deformable image registration using a Modified Demons algorithm yields clinically acceptable results and time-saving benefits in contouring that improve clinical workflow. The study also showed that it is feasible to incorporate deformable image registration as part of an adaptive radiotherapy strategy for head and neck cancer, provided further studies are designed to carry out accurate and verifiable dose deformation.

Outdoor tracking and registration are important enabling technologies for mobile augmented reality. Sensor fusion and image processing can be used to improve global tracking and registration for low-cost mobile devices with limited computational power and sensor accuracy. Prior research has confirmed the benefits of this approach with high-end hardware, however the methods previously used are not ideal for current consumer mobile devices. We discuss the development of a hybrid tracking and registration algorithm that combines multiple sensors and image processing to improve on existing work in both performance and accuracy. As part of this, we developed the Transform Flow toolkit, which is one of the first open source systems for developing and quantifiably evaluating mobile AR tracking algorithms. We used this system to compare our proposed hybrid tracking algorithm with a purely sensor based approach, and to perform a user study to analyse the effects of improved precision on real world tracking tasks. Our results show that our implementation is an improvement over a purely sensor fusion based approach; accuracy is improved up to 25x in some cases with only 2-4ms additional processing per frame, in comparison with other algorithms which can take over 300ms.

The modern day radiotherapy treatments are almost exclusively based on computed tomography (CT) images. The CT images are acquired using x-rays, and therefore reflect the radiation interaction properties of the material. This information is used to perform accurate dose calculation by the treatment planning system, and the data is also well suited for creating digitally reconstructed radiographs for comparing patient set up at the treatment machine where x-ray images are routinely acquired for this purpose. The magnetic resonance (MR) scanner has many attractive features for radiotherapy purposes. The soft tissue contrast as compared to CT is far superior, and it is possible to vary the sequences in order to visualize different anatomical and physiological properties of an organ. Both of these properties may contribute to an increase in accuracy of radiotherapy treatment. Using the MR images by themselves for treatment planning is, however, problematic. MR data reflects the magnetic properties of protons, and thus have no connection to the radiointeraction properties of the material. MRI also has inherent difficulty in imaging bone, which will appear in images as areas of no signal similar to air. This makes both dose calculation and patient positioning at the treatment machine troublesome. There are several clinics that use MR images together with CT images to perform treatment planning. The images are registered to a common coordinate system, a process often described as image fusion. In these cases, the MR images are primarily used for target definition and the CT images are used for dose calculations. This method is now not ideal, however, since the image fusion may introduce systematic uncertainties into the treatment due to the fact that the tumor is often able to move relatively freely with respect to the patients’ bony anatomy and outer contour, especially when the image registration algorithms take the entire patient anatomy in the volume of interest into account. The work presented in the thesis “Integration of MRI into the radiotherapy workflow” aim towards investigating the possibilities of workflows based entirely on MRI without using image registration, as well as workflows using image registration methods that are better suited for targets that can move with respect to surrounding bony anatomy, such as the prostate. / Modern strålterapi av cancer baseras nästan helt på datortomografiska (CT) bilder. CT bilder tas med hjälp av röntgenfotoner, och återger därför hur det avbildade materialet växelverkar med strålning. Denna information används för att utföra noggranna dosberäkningar i ett dosplaneringssystem, och data från CT bilder lämpar sig också väl för att skapa digitalt rekonstruerade röntgenbilder vilka kan användas för att verifiera patientens position vid behandling. Bildgivande magnetresonanstomografi (MRI) har många egenskaper som är intressanta för radioterapi. Mjukdelskontrasten i MR bilder är överlägsen CT, och det är möjligt att i stor utstäckning variera sekvensparametrar för att synliggöra olika anatomiska och funktionella attribut hos ett organ. Dessa bägge egenskaper kan bidra till ökad noggrannhet i strålbehandling av cancer. Att använda enbart MR bilder som planeringsunderlag för radioterapi är dock problematiskt. MR data reflekterar magnetiska attribut hos protoner, och har därför ingen koppling till materialets egenskaper då det gäller strålningsväxelverkan. Dessutom är det komplicerat att avbilda ben med MR; ben uppträder som områden av signalförlust i bilderna, på samma sätt som luft gör. Detta gör det svårt att utföra noggranna dosberäkningar och positionera patienten vid behandling. Många moderna kliniker använder redan idag MR tillsammans med CT under dosplanering. Bilderna registreras till ett gemensamt koordinatsystem i en process som kallas bildfusion. I dessa fall används MR bilderna primärt som underlag för utlinjering av tumör, eller target, och CT bilderna används som grund för dosberäkningar. Denna metod är dock inte ideal, då bildregistreringen kan införa systematiska geometriska fel i behandlingen. Detta på grund av att tumörer ofta är fria att röra sig relativt patientens skelett och yttre kontur, och många bildregistreringsalgoritmer tar hänsyn till hela bildvolymen. Arbetet som presenteras i denna avhandling syftar till att undersöka möjligheterna med arbetsflöden som baseras helt på MR data utan bildregistrering, samt arbetsflöden som använder bildregistrerings-algoritmer som är bättre anpassade för tumörer som kan röra sig i förhållande till patientens övriga anatomi, som till exempel prostatacancer.

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Inaccurate dose calculation in radiotherapy can lead to errors in treatment delivery and evaluation of treatment efficacy. Respiration can cause of intra-fractional motions, leading to uncertainties in tumor targeting. These motions should therefore be included in dose calculation. The finite element method-based deformable registration platform MORFEUS is able to accurately quantify organ deformations. The dose accumulation algorithm included in MORFEUS takes organ deformation and tumor movement into account. This study has experimentally validated this dose accumulation algorithm by combining 3D gel dosimetry, respiratory motion-mimicking actuation mechanism, and finite element analysis. Results have shown that within the intrinsic measurement uncertainties of gel dosimetry, under normal conformal dose distribution conditions, more than 90% of the voxels in MORFEUS generated dose grids have met the criterion analogous to the gamma test. The average (SD) distance between selected pairs of isodose surfaces on the gel and MORFEUS dose distributions is 0.12 (0.08) cm.

Effective early detection and monitoring of patient response to cancer therapy is important for improved patient outcomes, avoiding unnecessary procedures and their associated toxicities, as well as the development of new therapies. Dynamic contrast-enhanced magnetic resonance imaging shows promise as a way to evaluate tumour vasculature and assess the efficacy of new anti-angiogenic drugs. However, unavoidable patient motion can decrease the accuracy of subsequent analyses rendering the data unusable. Motion correction algorithms are challenging to develop for contrast-enhanced data since intensity changes due to contrast-enhancement and patient motion must somehow be differentiated from one another. A novel method is presented that employs a motion-informed intensity correction in order to facilitate the registration of contrast enhanced data. The intensity correction simulates the presence or absence of contrast agent in the image volumes to be registered in an attempt to emulate the level of contrast-enhancement present in a single reference image volume.

Inaccurate dose calculation in radiotherapy can lead to errors in treatment delivery and evaluation of treatment efficacy. Respiration can cause of intra-fractional motions, leading to uncertainties in tumor targeting. These motions should therefore be included in dose calculation. The finite element method-based deformable registration platform MORFEUS is able to accurately quantify organ deformations. The dose accumulation algorithm included in MORFEUS takes organ deformation and tumor movement into account. This study has experimentally validated this dose accumulation algorithm by combining 3D gel dosimetry, respiratory motion-mimicking actuation mechanism, and finite element analysis. Results have shown that within the intrinsic measurement uncertainties of gel dosimetry, under normal conformal dose distribution conditions, more than 90% of the voxels in MORFEUS generated dose grids have met the criterion analogous to the gamma test. The average (SD) distance between selected pairs of isodose surfaces on the gel and MORFEUS dose distributions is 0.12 (0.08) cm.