A Probabilistic, Non-parametric Framework for Inter-modality Label Fusion

Juan Eugenio Iglesias, Mert Rory Sabuncu, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights in a principled way in inter-modality scenarios remains an open problem. Here we propose a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment. The method is based on a generative model of image data in which each intensity in the atlases has an associated conditional distribution of corresponding intensities in the target. The segmentation is computed using variational expectation maximization (VEM) in a Bayesian framework. The method was evaluated with a dataset of eight proton density weighted brain MRI scans with nine labeled structures of interest. The results show that the algorithm outperforms majority voting and a recently published inter-modality label fusion algorithm.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013 : 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part III
Publication date2013
ISBN (Print)978-3-642-40759-8
ISBN (Electronic)978-3-642-40760-4
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) - Nagoya, Japan
Duration: 22 Sept 201326 Sept 2013


Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013)
Internet address
SeriesLecture Notes in Computer Science


  • Algorithms
  • Channel estimation
  • Magnetic resonance imaging
  • Medical image processing
  • Image segmentation


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