DocumentCode
2973107
Title
Substructural segmentation based on regional shape differences
Author
Machado, Alexei M C ; Gee, James C. ; Campos, Mario F M
Author_Institution
Dept. of Comput. Sci., Pontifical Catholic Univ. of Minas Gerais, Belo Horizonte, Brazil
fYear
2002
fDate
2002
Firstpage
3
Lastpage
10
Abstract
This article presents a method for the segmentation of substructures based on exploratory factor analysis. In this approach, a set of high-dimensional shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to morphometric investigations.
Keywords
eigenvalues and eigenfunctions; image registration; image segmentation; exploratory factor analysis; high-dimensional shape-related variables; regional shape differences; substructural segmentation; Anatomy; Computer science; Data mining; Image analysis; Image registration; Image segmentation; Information analysis; Magnetic resonance imaging; Principal component analysis; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Graphics and Image Processing, 2002. Proceedings. XV Brazilian Symposium on
ISSN
1530-1834
Print_ISBN
0-7695-1846-X
Type
conf
DOI
10.1109/SIBGRA.2002.1167117
Filename
1167117
Link To Document