Format

Send to:

Choose Destination
See comment in PubMed Commons below

Non-rigid registration with missing correspondences in preoperative and postresection brain images.

Author information

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, USA. nicha.chitphakdithai@yale.edu

Abstract

Registration of preoperative and postresection images is often needed to evaluate the effectiveness of treatment. While several non-rigid registration methods exist, most would be unable to accurately align these types of datasets due to the absence of tissue in one image. Here we present a joint registration and segmentation algorithm which handles the missing correspondence problem. An intensity-based prior is used to aid in the segmentation of the resection region from voxels with valid correspondences in the two images. The problem is posed in a maximum a posteriori (MAP) framework and optimized using the expectation-maximization (EM) algorithm. Results on both synthetic and real data show our method improved image alignment compared to a traditional non-rigid registration algorithm as well as a method using a robust error kernel in the registration similarity metric.

PMID:
20879252
[PubMed - indexed for MEDLINE]
PMCID:
PMC3031159
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk