The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

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Building maps within an unknown environment while keeping track of the current position is a major step to accomplish safe and autonomous robot navigation. Within the last 20 years, Simultaneous Localization And Mapping (SLAM) became a topic of great interest in robotics. The basic idea of this technique is to combine proprioceptive robot motion information with external environmental information to minimize global positioning errors. Because the robot is moving in its environment, exteroceptive data comes from different points of view and must be expressed in the same coordinate system to be combined. The latter process is called registration. Iterative Closest Point (ICP) is a registration algorithm with very good performances in several 3D model reconstruction applications, and was recently applied to SLAM. However, SLAM has specific needs in terms of real-time and robustness comparatively to 3D model reconstructions, leaving room for specialized robotic mapping optimizations in relation to robot mapping. After reviewing existing SLAM approaches, this thesis introduces a new registration variant called Kd-ICP. This referencing technique iteratively decreases the error between misaligned point clouds without extracting specific environmental features. Results demonstrate that the new rejection technique used to achieve mapping registration is more robust to large initial positioning errors. Experiments with simulated and real environments suggest that Kd-ICP is more robust compared to other ICP variants. Moreover, the Kd-ICP is fast enough for real-time applications and is able to deal with sensor occlusions and partially overlapping maps. Realizing fast and robust local map registrations opens the door to new opportunities in SLAM. It becomes feasible to minimize the cumulation of robot positioning errors, to fuse local environmental information, to reduce memory usage when the robot is revisiting the same location. It is also possible to evaluate network constrains needed to minimize global mapping errors.

This thesis is devoted to dense deformable image registration/fusion using discrete methods. The main contribution of the thesis is a principled registration framework coupling iconic/geometric information through graph-based techniques. Such a formulation is derived from a pair-wise MRF view-point and solves both problems simultaneously while imposing consistency on their respective solutions. The proposed framework was used to cope with pair-wise image fusion (symmetric and asymmetric variants are proposed) as well as group-wise registration for population modeling. The main qualities of our framework lie in its computational efficiency and versatility. The discrete nature of the formulation renders the framework modular in terms of iconic similarity measures as well as landmark extraction and association techniques. Promising results using a standard benchmark database in optical flow estimation and 3D medical data demonstrate the potentials of our methods.

The preparation of newly qualified nurses has raised many professional debates and yet the ‘end product’ of nurse education, the qualified nurse continues to demonstrate knowledge and skill deficits in areas considered essential to patient care. Technological advances in an ever-changing and complex clinical environment mean that certain acute clinical skills have become routine for the qualified nurse and yet few educational institutions and NHS Trusts in the UK have seen the need to address this within the pre-registration nursing curricula. This study, questions whether the pre-registration nursing curricula is failing newly qualified nurses by not adequately preparing them to cope with the complexities of practical skills within the clinical environment. This skill deficit is rectified on qualifying when nurses rapidly equip themselves with skills that are considered essential for practice. Using a constructivist paradigm and a mixed methods research approach, the opinion of key stakeholders in pre-registration nurse education was sought. Focus groups and surveys were conducted with skills teachers to ascertain essential skills. Constructive alignment theory (Biggs 1999) was tested with two student cohorts from a pre-registration nursing programme (n=58). Comparisons were made between an experimental group who acquired certain skills during their pre-registration programme and a control group who had not acquired those additional skills. The programme was evaluated using Stake’s (1967) countenance model of evaluation. Data were analysed using SPSS, constant comparative analysis and triangulation. The findings confirmed that nurses should acquire the skills investigated in this study, but differences of opinion were found as to when this was acceptable. Favourable results for the experimental group were demonstrated indicating the need to provide nurses with the additional skills prior to qualifying. The study also identified other like-minded UK nurse educators who had been innovative with their skills’ curricula. Nursing curricula can be successfully underpinned by an educational theory such as constructive alignment providing added value to the learner and enablingnurses to enter the profession fit for practice and purpose. To further enhance the quality and standard of provision, the following are recommended: strengthening the collaborative relationships between the key stakeholders for nurse education, as well as promoting interprofessional learning and skills development. This will help improve the international credibility for the UK skills curricula.

The digital photography is still trying to catch-up with its analogous counterpart and recording light direction is one of the most recent area of interest. The first and still the only one light-field camera for consumers, the Lytro camera, has reached market in 2011. This work introduces the light-field theory and recording with special emphasis on illustrating the principles in 2D, gives an overview of current hardware and ongoing research in the area and analyses the Lytro camera itself, describing the closed file formats and protocols it uses so that further research can be conducted. An important contribution of the work is a .NET portable library for developers, supplemented by a file editor as well as an application for wireless communication with the camera based on the library. Finally, the theory is used to discuss implications for light-field registration and linear panoramas. Powered by TCPDF (www.tcpdf.org)

Approved for public release; distribution is unlimited / Image navigation is critical to the effective use of digital imagery for meteorological and oceanographic studies. This thesis reviews various methods used to navigate imagery to the earth and investigates the accuracy of the Naval Postgraduate School (NPS) model. An explanation of how the NPS navigation process works is included for completeness. Results from 2 2 separate runs of the NPS model are studied. / http://archive.org/details/accuracyofsatell00beth / Captain, United States Marine Corps

This report is the result of a master thesis made by two students at Linköping University. The aim was to find an image registration method for visual and infrared images and to find an error measure for grading the registration performance. In practice this could be used for position determination by registering the infrared image taken at the current position to a set of visual images with known positions and determining which visual image matches the best. Two methods were tried, using different image feature extractors and different ways to match the features. The first method used phase information in the images to generate soft features and then minimised the square error of the optical flow equation to estimate the transformation between the visual and infrared image. The second method used the Canny edge detector to extract hard features from the images and Chamfer distance as an error measure. Both methods were evaluated for registration as well as position determination and yielded promising results. However, the performance of both methods was image dependent. The soft edge method proved to be more robust and precise and worked better than the hard edge method for both registration and position determination.

Wu, Xixuan. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 115-128). / Abstracts also in Chinese. / Title from PDF title page (viewed on 26, October, 2016).

" A novel Markov Random Field (MRF) based method for the mosaicing of 3D ultrasound volumes is presented in this dissertation. The motivation for this work is the production of training volumes for an affordable ultrasound simulator, which offers a low-cost/portable training solution for new users of diagnostic ultrasound, by providing the scanning experience essential for developing the necessary psycho-motor skills. It also has the potential for introducing ultrasound instruction into medical education curriculums. The interest in ultrasound training stems in part from the widespread adoption of point-of-care scanners, i.e. low cost portable ultrasound scanning systems in the medical community. This work develops a novel approach for producing 3D composite image volumes and validates the approach using clinically acquired fetal images from the obstetrics department at the University of Massachusetts Medical School (UMMS). Results using the Visible Human Female dataset as well as an abdominal trauma phantom are also presented. The process is broken down into five distinct steps, which include individual 3D volume acquisition, rigid registration, calculation of a mosaicing function, group-wise non-rigid registration, and finally blending. Each of these steps, common in medical image processing, has been investigated in the context of ultrasound mosaicing and has resulted in improved algorithms. Rigid and non-rigid registration methods are analyzed in a probabilistic framework and their sensitivity to ultrasound shadowing artifacts is studied. The group-wise non-rigid registration problem is initially formulated as a maximum likelihood estimation, where the joint probability density function is comprised of the partially overlapping ultrasound image volumes. This expression is simplified using a block-matching methodology and the resulting discrete registration energy is shown to be equivalent to a Markov Random Field. Graph based methods common in computer vision are then used for optimization, resulting in a set of transformations that bring the overlapping volumes into alignment. This optimization is parallelized using a fusion approach, where the registration problem is divided into 8 independent sub-problems whose solutions are fused together at the end of each iteration. This method provided a speedup factor of 3.91 over the single threaded approach with no noticeable reduction in accuracy during our simulations. Furthermore, the registration problem is simplified by introducing a mosaicing function, which partitions the composite volume into regions filled with data from unique partially overlapping source volumes. This mosaicing functions attempts to minimize intensity and gradient differences between adjacent sources in the composite volume. Experimental results to demonstrate the performance of the group-wise registration algorithm are also presented. This algorithm is initially tested on deformed abdominal image volumes generated using a finite element model of the Visible Human Female to show the accuracy of its calculated displacement fields. In addition, the algorithm is evaluated using real ultrasound data from an abdominal phantom. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult. Our solution to blending, which is the final step of the mosaicing process, is also discussed. The trainee will have a better experience if the volume boundaries are visually seamless, and this usually requires some blending prior to stitching. Also, regions of the volume where no data was collected during scanning should have an ultrasound-like appearance before being displayed in the simulator. This ensures the trainee's visual experience isn't degraded by unrealistic images. A discrete Poisson approach has been adapted to accomplish these tasks. Following this, we will describe how a 4D fetal heart image volume can be constructed from swept 2D ultrasound. A 4D probe, such as the Philips X6-1 xMATRIX Array, would make this task simpler as it can acquire 3D ultrasound volumes of the fetal heart in real-time; However, probes such as these aren't widespread yet. Once the theory has been introduced, we will describe the clinical component of this dissertation. For the purpose of acquiring actual clinical ultrasound data, from which training datasets were produced, 11 pregnant subjects were scanned by experienced sonographers at the UMMS following an approved IRB protocol. First, we will discuss the software/hardware configuration that was used to conduct these scans, which included some custom mechanical design. With the data collected using this arrangement we generated seamless 3D fetal mosaics, that is, the training datasets, loaded them into our ultrasound training simulator, and then subsequently had them evaluated by the sonographers at the UMMS for accuracy. These mosaics were constructed from the raw scan data using the techniques previously introduced. Specific training objectives were established based on the input from our collaborators in the obstetrics sonography group. Important fetal measurements are reviewed, which form the basis for training in obstetrics ultrasound. Finally clinical images demonstrating the sonographer making fetal measurements in practice, which were acquired directly by the Philips iU22 ultrasound machine from one of our 11 subjects, are compared with screenshots of corresponding images produced by our simulator. "