A Markov random field approach to group-wise registration/mosaicing with application to ultrasound.
Journal:
Medical image analysis
Published Date:
Jun 3, 2015
Abstract
In this paper we present a group-wise non-rigid registration/mosaicing algorithm based on block-matching, which is developed within a probabilistic framework. The discrete form of its energy functional is linked to a Markov Random Field (MRF) containing double and triple cliques, which can be effectively optimized using modern MRF optimization algorithms popular in computer vision. Also, the registration problem is simplified by introducing a mosaicing function which partitions the composite volume into regions filled with data from unique, partially overlapping source volumes. Ultrasound confidence maps are incorporated into the registration framework in order to give accurate results in the presence of image artifacts. The algorithm is initially tested on simulated images where shadows have been generated. Also, validation results for the group-wise registration algorithm using real ultrasound data from an abdominal phantom are presented. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult. In addition, results are presented suggesting that a fusion approach to MRF registration can produce accurate displacement fields much faster than standard approaches.
Authors
Keywords
Algorithms
Computer Simulation
Data Interpretation, Statistical
Female
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Imaging, Three-Dimensional
Machine Learning
Markov Chains
Models, Statistical
Pattern Recognition, Automated
Pregnancy
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
Ultrasonography