Multi-subject variational registration for probabilistic unbiased atlas generation

This paper introduces a new metric to gather a large collection of segmented images into a same reference system. Different positions for each subject (pose parameters) as well as high energy shape variations need to be compensated before performing statistical analysis (like principal components an...

Full description

Saved in:
Bibliographic Details
Main Authors: De Craene, M., du Bois d'Aische, A., Macq, B., Warfield, S.K.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Histograms
Hospitals
Image databases
Laboratories
Mutual information
Pediatrics
Probability distribution
Radiology
Stochastic processes
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces a new metric to gather a large collection of segmented images into a same reference system. Different positions for each subject (pose parameters) as well as high energy shape variations need to be compensated before performing statistical analysis (like principal components analysis) on the database. The atlas is obtained as the hidden variable of an expectation-maximization (EM), looking for the right signal intensity at each voxel in the collection of subjects. Each subject is aligned on the current probabilistic atlas by maximizing mutual information. A fast stochastic optimization algorithm is used for estimating pose and scale parameters and a variational approach have been designed to estimate non-rigid transformations. We illustrate the effectiveness of this method for the alignment of 31 brain segmented in 4 labels: background, white and gray matter and ventricles. Our approach has the advantage of keeping a reasonably low complexity even for large databases.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2005.1530463