Image registration using robust M-estimators
Pattern Recognition Letters
A Non-rigid Registration Framework That Accommodates Resection and Retraction
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
De-enhancing the dynamic contrast-enhanced breast MRI for robust registration
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Pairwise registration of images with missing correspondences due to resection
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Tracking metastatic brain tumors in longitudinal scans via joint image registration and labeling
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
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Registration of preoperative and postresection images is often needed to evaluate the effectiveness of treatment. While several nonrigid 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.