Paper Info
Title
3-D Deformable Registration of Medical Images Using a Statistical Atlas
Abstract
Registration between 3-D images of human anatomies enables cross-subject diagnosis. However, innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult. We characterize such anatomical variations to achieve accurate registration. We represent anatomical variations in the form of statistical models, and embed these statistics into a 3-D digital brain atlas which we use as a reference. These models are built by registering a training set of brain MRI volumes with the atlas. This associates each voxel in the atlas with multi-dimensional distributions of variations in intensity and geometry of the training set. We evaluate statistical properties of these distributions to build a statistical atlas. When we register the statistical atlas with a particular subject, the embedded statistics function as prior knowledge to guide the deformation process. This allows the deformation to tolerate variations between individuals while retaining discrimination between different structures. This method gives an overall voxel mis-classification rate of 2.9% on 40 test cases; this is a 34% error reduction over the perfor- mance of our previous algorithm without using anatomical knowledge. Besides achieving accurate registration, statistical models of anatomical variations also enable quantitative study of anatomical differences between populations.
Year
DOI
Venue
1999
10.1007/10704282_67
MICCAI
Keywords
Field
DocType
statistical atlas,3-d deformable registration,statistical model
Computer vision,Pattern recognition,Computer science,Medical image computing,Atlas (anatomy),Artificial intelligence,Anatomical structures
Conference
Volume
ISSN
ISBN
1679
0302-9743
3-540-66503-X
Citations 
PageRank 
References 
17
1.83
13
Authors
4
Name
Order
Citations
PageRank
Mei Chen1171.83
Takeo Kanade2250734203.02
Dean Pomerleau31039283.23
Jeff G. Schneider41616165.43