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dc.contributor.author Stark, Maya Belen Cervantes Gautschi
dc.date.accessioned 2018-06-22T16:46:27Z
dc.date.available 2018-06-22T16:46:27Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/10211.3/204021
dc.description.abstract The procedures for repairing or replacing prostheses used in total shoulder arthroplasty (TSA) vary depending on the particular model of the prosthesis. If the model of the prosthesis is unknown, identification is performed by medical professionals based on visual inspection of X-ray images. This process is tedious and time consuming; indicating an unmet need for a tool that will aid surgeons in identifying prostheses quickly and accurately. A preliminary step towards the creation of such a classification tool is the segmentation of the prosthesis. This thesis describes the design and implementation of a software solution to the problem of detection and segmentation of TSA implants in X-ray images. The method implemented uses the Hough transform for circles to locate the implant, followed by segmentation using a seeded region growing method. Validation is performed by comparison with manually segmented ground-truth images, and by visual inspection of the results. en_US
dc.format.extent xiv, 98 leaves en_US
dc.language.iso en_US en_US
dc.publisher San Francisco State University en_US
dc.rights Copyright by Maya Belen Cervantes Gautschi Stark, 2018 en_US
dc.source AS36 2018 CMPTR .S73
dc.title Automatic detection and segmentation of shoulder implants in x-ray images en_US
dc.type Thesis en_US
dc.contributor.department Computer Science en_US
dc.description.degree Computer Science


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