Geodesic regression of image and shape data for improved modeling of 4D trajectories

Update item information
Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Fishbaugh, James; Prestawa, Marcel; Durrieman, Stanley
Title Geodesic regression of image and shape data for improved modeling of 4D trajectories
Date 2014-01-01
Description A variety of regression schemes have been proposed on images or shapes, although available methods do not handle them jointly. In this paper, we present a framework for joint image and shape regression which incorporates images as well as anatomical shape information in a consistent manner. Evolution is described by a generative model that is the analog of linear regression, which is fully characterized by baseline images and shapes (intercept) and initial momenta vectors (slope). Further, our framework adopts a control point parameterization of deformations, where the dimensionality of the deformation is determined by the complexity of anatomical changes in time rather than the sampling of the image and/or the geometric data. We derive a gradient descent algorithm which simultaneously estimates baseline images and shapes, location of control points, and momenta. Experiments on real medical data demonstrate that our framework effectively combines image and shape information, resulting in improved modeling of 4D (3D space + time) trajectories.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 385
Last Page 388
Language eng
Bibliographic Citation Fishbaugh, J., Prestawa, M., Gerig, G., & Durrieman, S. (2014). Geodesic regression of image and shape data for improved modeling of 4D trajectories. IEEE International Symposium on Biomedical Imaging (ISBI), 385-8.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,223,409 bytes
Identifier uspace,18961
ARK ark:/87278/s6bw0rrj
Setname ir_uspace
Date Created 2014-10-17
Date Modified 2014-10-17
ID 712693
Reference URL https://collections.lib.utah.edu/ark:/87278/s6bw0rrj
Back to Search Results