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dc.contributor.advisorPoole, Doctor Ian
dc.contributor.advisorBelyaev, Doctor Alex
dc.contributor.authorO'Neil, Alison
dc.date.accessioned2019-05-10T08:44:02Z
dc.date.available2019-05-10T08:44:02Z
dc.date.issued2016-10
dc.identifier.urihttp://hdl.handle.net/10399/3502
dc.description.abstractDetection and localisation of anatomical structures is extremely helpful for many image analysis algorithms. This thesis is concerned with the automatic identification of landmark points, anatomical regions and vessel centre lines in three-dimensional medical datasets. We examine how machine learning and atlas-based ideas may be combined to produce efficient, context-aware algorithms. For the problem of anatomical landmark detection, we develop an analog to the idea of autocontext, termed atlas location autocontext, whereby spatial context is iteratively learnt by the machine learning algorithm as part of a feedback loop. We then extend our anatomical landmark detection algorithm from Computed Tomography to Magnetic Resonance images, using image features based on histograms of oriented gradients. A cross-modality landmark detector is demonstrated using unsigned gradient orientations. The problem of brain parcellation is approached by independently training a random forest and a multi-atlas segmentation algorithm, then combining them by a simple Bayesian product operation. It is shown that, given classifiers providing complementary information, the hybrid classifier provides a superior result. The Bayesian product method of combination outperforms simple averaging where the classifiers are sufficiently independent. Finally, we present a system for identifying and tracking major arteries in Magnetic Resonance Angiography datasets, using automatically detected vascular landmarks to seed the tracking. Knowledge of individual vessel characteristics is employed to guide the tracking algorithm by two means. Firstly, the data is pre-processed using a top-hat transform of size corresponding to the vessel diameter. Secondly, a vascular atlas is generated to inform the cost function employed in the minimum path algorithm. Fully automatic tracking of the major arteries of the body is satisfactorily demonstrated.
dc.language.isoenen_US
dc.publisherHeriot-Watt Universityen_US
dc.publisherEngineering and Physical Sciencesen_US
dc.rightsAll items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.
dc.titleDetection of anatomical structures in medical datasetsen_US
dc.typeThesisen_US


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