Publication Type |
journal article |
School or College |
<blank> |
Department |
<blank> |
Creator |
Gerig, Guido |
Other Author |
Styner, Martin; Xu, Shun; El-Sayed, Mohammed |
Title |
Correspondence evaluation in local shape analysis and structural subdivision |
Date |
2007-01-01 |
Description |
Regional volumetric and local shape analysis has become of increasing interest to the neuroimaging community due to the potential to locate morphological changes. In this paper we compare three common correspondence methods applied to two studies of hippocampal shape in schizophrenia: correspondence via deformable registration, spherical harmonics (SPHARM) and Minimum Description Length (MDL) optimization. These correspondence methods are evaluated in respect to local statistical shape analysis and structural subdivision analysis. Results show a non-negligible influence of the choice of correspondence especially in studies with low numbers of subjects. The differences are especially striking in the structural subdivision analysis and hints at a possible source for the diverging findings in many subdivision studies. Our comparative study is not meant to be exhaustive, but rather raises awareness of the issue and shows that assessing the validity of the correspondence is an important step. |
Type |
Text |
Publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
First Page |
1192 |
Last Page |
1195 |
Language |
eng |
Bibliographic Citation |
Styner, M., Xu, S., El-Sayed, M., & Gerig, G. (2007). Correspondence evaluation in local shape analysis and structural subdivision. roceedings of ISBI 2007, IEEE Press, 1192-5. |
Rights Management |
(c) 2007 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 |
772,885 bytes |
Identifier |
uspace,19263 |
ARK |
ark:/87278/s6v72tqs |
Setname |
ir_uspace |
ID |
712813 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6v72tqs |