Ultrasound is a favorable tool for intra-operative surgical guidance due to its fast imaging speed and non-invasive nature. However, deformations of the anatomy caused by breathing, heartbeat, and movement of the patient make it difficult to track the location of anatomical landmarks during intra-operative ultrasound-guided interventions. While elastic registration can be used to compensate for image misalignment, its adaptation for clinical use has only been gradual due to the lack of standardized guidelines to quantify the performance of different registration techniques. Evaluation of elastic registration algorithms is a difficult task since the point to point correspondence between images is usually unknown. This poses a major challenge in the validation of non-rigid registration techniques for performance comparisons. Current validation guidelines for non-rigid registration algorithms exist for the comparison of techniques for magnetic resonance images of the brain. These frameworks provide users with standardized brain datasets and performance measures based on brain region alignment, intensity differences between images, and inverse consistency of transformations. These metrics may not all be suitable for ultrasound registration algorithms due to the different properties of the imaging modalities. Furthermore, other metrics are required for validating the registration performance on different anatomical images with large deformations such as the liver. This work presents a validation framework dedicated for ultrasound elastic registration algorithms. Quantitative validation metrics are evaluated for ultrasound images. These include a simulation technique to measure registration accuracy, a segmentation algorithm to extract anatomical landmarks to measure feature overlap, and a technique to measure the alignment of images using similarity metrics. An extensive study of an ultrasound temporal registration algorithm is conducted using the proposed validation framework. Experiments are performed on a large database of 2D and 3D US images of the carotid artery and the liver to assess the performance of this algorithm. In addition, two graphical user interfaces which integrate the image registration and segmentation techniques have been developed to visualize the performance of these algorithms on ultrasound images captured in real time. In the future, these interfaces may be used to enhance ultrasound examination. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-10-24 22:35:20.875

L’évaluation de la gravité et la surveillance des maladies pulmonaires chroniques représentent deux challenges importants pour la prise en charge des patients et l’évaluation des traitements. La surveillance repose principalement sur les données fonctionnelles respiratoires mais l’évaluation morphologique reste un point essentiel pour le diagnostic et l’évaluation de sévérité. Dans la première partie de cette thèse, nous proposons différents modèles pour quantifier la sévérité de pathologies bronchiques chroniques au scanner. Une approche simple par seuillage adaptatif et une méthode plus sophistiquée de radiomique sont évaluées Dans la seconde partie, nous évaluons une méthode d’apprentissage profond pour contourer automatiquement l’atteinte fibrosante de la sclérodermie en scanner. Nous combinons le recalage élastique vers différents atlas morphologiques thoraciques et l’apprentissage profond pour développer un modèle dont les performances sont équivalentes à celles des radiologues. Dans la dernière partie, nous démontrons que l’étude de la déformation pulmonaire en IRM entre inspiration et expiration peut être utilisée pour repérer les régions pulmonaires en transformation fibreuse, moins déformables au cours de la respiration, et qu’en scanner, l’évaluation de la déformation entre des examens successifs de suivi peut diagnostiquer l’aggravation fibreuse chez les patients sclérodermiques. / Disease staging and monitoring of chronic lung diseases are two major challenges for patient care and evaluation of new therapies. Monitoring mainly relies on pulmonary function testing but morphological assessment is a key point for diagnosis and staging In the first part, we propose different models to score bronchial disease severity on computed tomography (CT) scan. A simple thresholding approach using adapted thresholds and a more sophisticated machine learning approach with radiomics are evaluated In the second part, we evaluate deep learning methods to segment lung fibrosis on chest CT scans in patients with systemic sclerosis. We combine elastic registration to atlases of different thoracic morphology and deep learning to produce a model performing as well as radiologists In the last part of the thesis, we demonstrate that lung deformation assessment between inspiratory and expiratory magnetic resonance images can be used to depict fibrotic lung areas, which show less deformation during respiration and that CT assessment of lung deformation on serial CT scans can be used to diagnose lung fibrosis worsening

This work refers existing solutions and options for the task registration of photos to 3D model based on the previous knowledge of the geographic position of the camera. The contribution of the work are new ways and possibilities of the solution with the usage of graph algorithms. In this area, the work interests are useful points of interest detection in input data, a construction of graphs and graph matching possibilities.