Paper Info
Title
Segmentation of cardiac mr images: An active appearance model approach
Abstract
Active Appearence Models (AAM), which have been recently introduced by Cootes et al., describe the shape of objects and grey level appearance from a set of example images. An AAM is created from user-placed contours defining the shape of objects of interest in each training image. The information about shape changes observed in the training set is used to model the shape variation. Principle component analysis (PCA) is utilized to model gray level variation observed in the training set. The resulting model describes objects as a linear combination of eigen vectors both in shape and gray levels applied to the mean image. The main purpose of this work is to investigate the clinical potential of AAMs for segmentation of cardiovascular MR images acquired in routine clinical practice. An AAM was constructed using 102 end-diastolic short-axis cardiac MR images at the papillary muscle level from normals and patients with varying pathologies. The resulting AAM. is a compact representation consisting of a mean image and a limited number of coefficients of eigen vectors, representing 97% of shape and gray level variation observed in the training set. The segmentation performance is tested in 60 end-diastolic short-axis cardiac MR images from different patients.
Year
DOI
Venue
2000
10.1117/12.387684
Proceedings of SPIE
Keywords
Field
DocType
active appearence models,active shape models,point distribution models,principal component analysis
Training set,Point distribution model,Linear combination,Computer vision,Segmentation,Clinical Practice,Image processing,Active appearance model,Artificial intelligence,Geography,Principal component analysis
Conference
Volume
ISSN
Citations 
3979
0277-786X
21
PageRank 
References 
Authors
2.03
0
6
Name
Order
Citations
PageRank
S. Mitchell1608.32
B.P.F. Lelieveldt21331115.59
R. Van Der Geest3222.53
J. Schaap4212.03
J. Reiber5222.53
Milan Sonka623149.15