In the field of medical imaging, image registration methods are useful for many applications such as inter- and intra-subject morphological comparisons, creation of population atlases, delivery of precision therapies, etc. A user may want to know which is the most suitable registration algorithm that would work best for the intended application, but the vastness of medical image registration applications makes evaluation and comparison of image registration performance a non-trivial task. In general, evaluating image registration performance is not straightforward because in most image registration applications there is an absence of “Gold Standard” or ground truth correspondence map to compare against. It is therefore the primary goal of this thesis work to provide a means for recommending the most appropriate registration algorithm for a given task. One of the contributions of this thesis is to examine image registration algorithm performance at the component level. Another contribution of this thesis is to catalog the benefits and limitations of many of the most commonly used image registration evaluation approaches. One incremental contribution of this thesis was to demonstrate how existing evaluation methods can be applied in the midpoint coordinate system to evaluate some symmetric image registration algorithms such as the SyN registration algorithm. Finally, a major contribution of this thesis was to develop tools to evaluate and visualize 2D and 3D image registration shape collapse. This thesis demonstrates that many current diffeomorphic image registration algorithms suffer from the collapse problem, provides the first visualizations of the collapse problem in 3D for simple shapes and real human brain MR images, and provides the first experiments that demonstrate how adjusting image registration parameters can mitigate the collapse problem to some extent.

This dissertation provides the foundations of computer-based automated phenotyping methods for analyzing 3D images of mouse embryos. A group-wise registration technique was utilized and optimized and computerized methods were employed for analysis of 3D MRI images of mouse embryos. The assumption that embryo anatomy is highly conserved among genetically identical specimens was verified. The group-wise registration approach was used to align a group of embryos from the 129S1/SvImJ (129Sv) strain as well as a group of C57BL/6J (C57) embryos. Finally, we shed some light on some of the morphological differences between the 129Sv and C57 strains using automated techniques.

This dissertation provides the foundations of computer-based automated phenotyping methods for analyzing 3D images of mouse embryos. A group-wise registration technique was utilized and optimized and computerized methods were employed for analysis of 3D MRI images of mouse embryos. The assumption that embryo anatomy is highly conserved among genetically identical specimens was verified. The group-wise registration approach was used to align a group of embryos from the 129S1/SvImJ (129Sv) strain as well as a group of C57BL/6J (C57) embryos. Finally, we shed some light on some of the morphological differences between the 129Sv and C57 strains using automated techniques.

Registration means to bring two or more shapes into a suitable relative configuration (position and orientation). In its major applications like 3D scan alignment, the aim is to coalesce data and regions originating from the same physical region have similar local form. So, the correspondence between shapes is discoverable from the shapes themselves, and the registration makes corresponding regions coincide. This work concerns the registration of shapes to satisfy a purpose or intent, not involving data integration. Regions relevant to the purpose are marked as patches correspondingly on two input 3D meshes of objects. Then, a method of registration is used to obtain the suitable configuration. Three methods of registration are explored in the present work. The first method of registration is to align intrinsic co-ordinate frames defined on the shapes. This is used in a scenario of comparison of shapes with dissimilar local form, which are to be aligned as an expert requires, as in the comparison of dental casts and apple bitemarks in forensics. Regions recognized in dentistry are marked as patches on the cast and bitemark shapes by a dentist. From these, an intrinsic frame is defined and aligned to bring the shapes close. The alignment is used to calculate distortion of a deteriorated bitemark. Another application of frame alignment is the analysis of shape variation of contours in a population for wearable product design. A frame based on anthropometric landmarks is used to construct the contours of the product's interface with the body-part, analyze its spread through a 2D grid-statistics method, and construct the interface shape. The frame helps assess the fit of the constructed shape on an individual. The method is demonstrated with respirator masks. Frame-based alignment is seen to give unsatisfactory results with head shapes for motorcycle-helmet interior design, as it does not adequately describe the helmet-head interaction. This inspires the second method of registration. The second method of registration is the biased minimization of distance between corresponding patches on the shapes, by weighting patches to indicate their importance in the registration. The method is used to assess the small deviation of precisely-known quantities in shapes, such as in manufactured part inspection. Here, the patches marked are grouped, and the part and model shapes registered at patches in the combinations of groups, by giving a binary weighting of 1 to these patches and 0 to others. The deviation of every patch across the registrations at multiple datum systems is tabulated and analyzed to infer errors. The method is exemplified with welded bars and bent-pipes. In the analysis of head-shape variation in a population to create headforms for wearable products, the deviations are large and not precisely known. So, the head shapes are registered at patches on regions pertinent to the product's functioning, with a relatively higher weight for a reference patch. A 3D grid-statistics method is used to analyze the shapes' spread and arrive at the headform shapes. The selection of head form for a given head shape is also treated. The method is demonstrated with motorcycle helmets and respirator masks. Biased distance-minimization is applied to obtain the mechanical assembly of part meshes. Different schemes of marking patches are tested as cases. The method leads to both intended and unintended final configurations, prompting for a better objective in registration. Thus, the third method of registration, that of normals is proposed; this happens in a transformed space. By analyzing the nature of assembly in CAD systems, the face-normals of the mesh are used to obtain the intended orientation of parts. The normals of corresponding patches are registered using three methods of registration, namely on a unit-sphere, of unit-normals, and spherical co-ordinates of normals. In each method, the optimal transformation is suitably converted to be applied on the actual part shape in 3D. Unit-normal alignment gives sensible results, while the other two lead to skewed final orientations. This is attributed to the nature of the space of registration. The methods are applied to examples involving different assembly relations, such as alignment of holes. On the whole, it is shown that correspondence embodies the knowledge of importance of regions on shapes for a purpose. The registration method should lead to an apt shape placement, which need not always mean coincidence. In essence, correspondence denotes 'what' regions are of relevance, and registration, 'how' to get the relative configuration satisfying a purpose or intent.

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Call number: LD2668 .T4 1978 A95 / Master of Science

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Spine needle injections are widely applied to alleviate pain and to remove nerve sensation through anesthesia. Current treatment is performed either blindly having no image guidance or using fluoroscopy or computed tomography (CT). Both CT and fluoroscopy guidance expose patients to ionizing radiation. Alternatively, ultrasound (US) guidance for spine needle procedures is becoming more prevalent since US is a non-ionizing and more accessible image modality. An inherent challenge to US imaging of the spine is the acoustic shadows created by the bony structures of the vertebra limiting visibility. It is challenging to use US as the sole imaging modality for intraoperative guidance of spine needle injections. However, it is possible to enhance the anatomical information through a preoperative diagnostic CT. To achieve this, image registration between the CT and the US images is proposed in this thesis. Image registration integrates the anatomical information from the CT with the US images. The aligned CT augments anatomical visualization for the clinician during spinal interventions. To align the preoperative CT and intraoperative US, a novel registration pipeline is presented that involves automatic global and multi-vertebrae registration. The registration pipeline is composed of two distinct phases: preoperative and intraoperative. Preoperatively, artificial spring points are selected between adjacent vertebrae. Intraoperatively, the lumbar spine is first aligned between the CT and US followed by a multi-vertebrae registration. The artificial springs are used to constrain the movement of the individually transformed vertebrae to ensure the optimal alignment is a pose of the lumbar spine that is physically possible. Validation of the algorithm is performed on five clinical patient datasets. A protocol for US data collection was created to eliminate variability in the quality of acquired US images. The registration pipeline was able to register the datasets from initial misalignments of up to 25 mm with a mean TRE of 1.17 mm. From these results, it is evident that the proposed registration pipeline offers a robust registration between clinical CT and US data. / Thesis (Master, Computing) -- Queen's University, 2013-08-19 12:50:54.521

This research has focused on a detailed exploration of undergraduate nursing students' development for registration and their future employability potential. There has been a particular emphasis on probing whether there is value in being a nursing graduate, within this. In the study employability refers to graduating students' preparedness to contribute to their immediate and longer term working lives, using the combination of operational and academic competence, self-efficacy and potential for further development through reflection and lifelong learning. A review of literature has revealed that there is a general paucity of any type of related published research from the within the UK and elsewhere, especially qualitatively-based research. In particular, no research could be found which has explored in-depth, as its primary aim, what nursing graduateness constitutes at the point of graduation and registration as a nurse and, whether it contributes to the employability potential of graduate nurses for both basic and advanced practice. A qualitative, instrumental case study was applied as the research design to achieve depth of focus for this inquiry, in order to meet the aims of the research. Findings from the study have uncovered a wide range of graduate attributes which were identified within participating students' development and perceived employability potential. From this a model of nursing graduateness has been proposed. Findings also indicated that changing healthcare needs within society coupled with the recent reforms in healthcare, NHS policy and the nursing role have placed greater emphasis than ever before on such graduate attributes within nursing as higher order thinking for effective nursing practice. Recommendations have been put forward for nursing education practice and research.

Object Recognition is the art of localizing predefined objects in image sensor data. In this thesis a depth sensor was used which has the benefit that the 3D pose of the object can be estimated. This has applications in e.g. automatic manufacturing, where a robot picks up parts or tools with a robot arm. This master thesis presents an implementation and an evaluation of a system for object recognition of 3D models in depth sensor data. The system uses several depth images rendered from a 3D model and describes their characteristics using so-called feature descriptors. These are then matched with the descriptors of a scene depth image to find the 3D pose of the model in the scene. The pose estimate is then refined iteratively using a registration method. Different descriptors and registration methods are investigated. One of the main contributions of this thesis is that it compares two different types of descriptors, local and global, which has seen little attention in research. This is done for two different scene scenarios, and for different types of objects and depth sensors. The evaluation shows that global descriptors are fast and robust for objects with a smooth visible surface whereas the local descriptors perform better for larger objects in clutter and occlusion. This thesis also presents a novel global descriptor, the CESF, which is observed to be more robust than other global descriptors. As for the registration methods, the ICP is shown to perform most accurately and ICP point-to-plane more robust.