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

Med Image Comput Comput Assist Interv. 2010;13(Pt 1):367-74. doi: 10.1007/978-3-642-15705-9_45.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Magnetic Resonance Imaging / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique*