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Title: Automatic and accurate segmentation of thoratic aortic aneurysms from X-ray CT angiography
Author: Ferreira, Filipa
ISNI:       0000 0004 2743 9072
Awarding Body: Kingston University
Current Institution: Kingston University
Date of Award: 2012
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The scoped of this dissertation is to procure and propose a novel fully automated computer aided detection and measurement (CAD/CAM) system of thoracic aortic aneurysms. More explicitly, the objective of the algorithm is to facilitate the segmentation of the thoracic aorta, as accurately as possible and detection of possible existing aneurysms in the Computer Tomography Angiography (CT) images. In biomedical imaging, the manual examination and analysis of aortic aneurysms is a particularly laborious and time-consuming undertaking. Humans are susceptible to committing errors and their analysis is usually subjective and qualitative due to the inter- and intra-observer variability issue. Objective and quantitative analysis facilitated by the application developed in this project leads to a more accurate diagnostic decision by the physician. In this context, the project is concerned with the automatic analysis of thoracic aneurysms from CTA images. The project initially examines the theoretical background of the anatomy of the aorta and aneurysms. The concepts of image segmentation and, in particular, segmentation of vessels methods are reviewed. An algorithm is then developed and implemented, such that it will conform to the requirements put forth in the stated objectives. For purposes of testing the proposed approach, a significant amount of 3D, clinical CTA datasets of thoracic aorta form the framework of the CAD/CAM system. It is followed by presentation and discussion of the results. The system has been validated on a clinical dataset of30 eTA scans of which 28 eTA scans contained aneurysms. There were 30 eTA scans used as training dataset for parameter selection and another 30 eTA scans uses as a test dataset, in total 60 for clinical evaluation. The radiologist visually inspected the CAD and CAM component results and confirmed it correctly detected and segmented the T AA on all datasets, proving to have 100% sensitivity. We were able to conclude that there is distinct potential for.use of our fully automated CAD/CAM system in a real clinical setting. Although other CAD/CAM systems have been developed for other organ detection and even small sections of the thoracic aorta, to this date no fully automated CAD/CAM of the entire thoracic aorta has been developed hence its novelty. To facilitate the proposed CAD/CAM system is integrated in a Medical Images Processing, Seamless and Secure Sharing Platform (MIPS3) which is a friendly user interface that has been developed alongside with this project.
Supervisor: Qanadli, Salah ; Dehmeshki, Jamshid Sponsor: South West London Academic Network
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Biological sciences ; Computer science and informatics