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Feature-Based Correspondences to Infer the Location of Anatomical Landmarks

Tamburo, Robert Joseph (2006) Feature-Based Correspondences to Infer the Location of Anatomical Landmarks. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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A methodology has been developed for automatically determining inter-image correspondences between cliques of features extracted from a reference and a query image. Cliques consist of up to threefeatures and correspondences between them are determined via a hierarchy of similarity metrics based on the inherent properties of the features and geometric relationships between those features. As opposed to approaches that determine correspondences solely by voxel intensity, features that also include shape description are used. Specifically, medial-based features areemployed because they are sparse compared to the number of image voxels and can be automatically extracted from the image.The correspondence framework has been extended to automatically estimate the location of anatomical landmarks in the query image by adding landmarks to the cliques. Anatomical landmark locationsare then inferred from the reference image by maximizing landmark correspondences. The ability to infer landmark locations has provided a means to validate the correspondence framework in thepresence of structural variation between images. Moreover, automated landmark estimation imparts the user with anatomical information and can hypothetically be used to initialize andconstrain the search space of segmentation and registration methods.Methods developed in this dissertation were applied to simulated MRI brain images, synthetic images, and images constructed from several variations of a parametric model. Results indicate that the methods are invariant to global translation and rotation and can operate in the presence of structure variation between images.The automated landmark placement method was shown to be accurate as compared to ground-truth that was established both parametrically and manually. It is envisioned that these automated methods could prove useful for alleviating time-consuming and tedious tasks in applications that currently require manual input, and eliminate intra-user subjectivity.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Tamburo, Robert Josephrjtst21@pitt.eduRJTST21
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairStetten,
Committee MemberLi, Ching-Chungccl@engr.pitt.eduCCL
Committee MemberBoada,
Committee MemberBoston, J Robertboston@ee.pitt.eduBBN
Date: 27 September 2006
Date Type: Completion
Defense Date: 24 April 2006
Approval Date: 27 September 2006
Submission Date: 5 May 2006
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: anatomical landmark; feature-based correspondences
Other ID:, etd-05052006-134756
Date Deposited: 10 Nov 2011 19:43
Last Modified: 15 Nov 2016 13:43


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