Predicting Femoral Geometry from Anatomical Features

dc.contributor.authorGrondin Lazazzera, Jeromeen
dc.contributor.supervisorStewart, Jamesen
dc.date2014-04-29 21:53:10.809's University at Kingstonen
dc.descriptionThesis (Master, Computing) -- Queen's University, 2014-04-29 21:53:10.809en
dc.description.abstractKnee replacement surgery is a common orthopaedic procedure that greatly benefits from a three-dimensional geometric representation of a patient's knee bone obtained from MR or CT data. The use of these image modalities pose the following challenges: (i) high imaging cost; (ii) long wait times; (iii) limited availability and (iv) in the latter, large exposure to ionizing radiation. Traditional approaches based on planar X-ray radiography are significantly less prone to these issues; however, they only provide two-dimensional information. This work presents a proof of concept study for generating patient-specific femoral bone shapes from a statistical shape atlas using anatomical features acquired from calibrated X-ray radiographs. Our hypothesis was: three-dimensional geometry can be reconstructed, within 2 millimeters RMS, by identifying features on two calibrated radiographs. We illustrate the feasibility of our approach with regards to acquiring features and the viability of reconstructing patient-specific bony anatomy. A set of reliable and relevant features is identified for which an acquisition protocol and user-interface was devised to minimize inter-observer variability. Both the data and methods used to construct the atlas are discussed as well generating shapes from features. The reconstructions accuracy was comparable to, albeit lower than, competing approaches that rely on two-dimensional bony contours.en
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectstatistical shape atlasen
dc.subjectanatomical featuresen
dc.titlePredicting Femoral Geometry from Anatomical Featuresen
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