A Markov random field approach to group-wise registration/mosaicing with application to ultrasound

• Published in: Medical image analysis Vol. 24; no. 1; pp. 106 - 124
• Main Authors: Kutarnia, Jason, Pedersen, Peder
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2015
• Subjects: Algorithms; Computer Simulation; Data Interpretation, Statistical; Discrete methods; Female; Group-wise registration; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Machine Learning; Markov Chains; Models, Statistical; Pattern Recognition, Automated - methods; Pregnancy; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Ultrasonography - methods; Ultrasound mosaicing; Group-wise registration; Ultrasound mosaicing; Discrete methods
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2015.05.011

•An efficient group-wise non-rigid registration algorithm which is robust to shadow artifacts.•Theory systematically developed and linked to a Markov random field.•Novel graph-based mosaicing algorithm presented to stitch overlapping fetal image volumes.•Optimization speed-up gained through fusion of registration results calculated in parallel.•Clinical stitching results incorporated into simulator and evaluated by obstetrics professionals. [Display omitted] 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.