Learning Likelihoods for Labeling (L3): A General Multi-Classifier Segmentation Algorithm

  • Neil I. Weisenfeld
  • Simon K. Warfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


PURPOSE: To develop an MRI segmentation method for brain tissues, regions, and substructures that yields improved classification accuracy. Current brain segmentation strategies include two complementary strategies. Multi-spectral classification techniques generate excellent segmentations for tissues with clear intensity contrast, but fail to identify structures defined largely by location, such as lobar parcellations and certain subcortical structures. Conversely, multi-template label fusion methods are excellent for structures defined largely by location, but perform poorly when segmenting structures that cannot be accurately identified through a consensus of registered templates. METHODS: We propose here a novel multi-classifier fusion algorithm with the advantages of both types of segmentation strategy. We illustrate and validate this algorithm using a group of 14 expertly hand-labeled images. RESULTS: Our method generated segmentations of cortical and subcortical structures that were more similar to hand-drawn segmentations than majority vote label fusion or a recently published intensity/label fusion method. CONCLUSIONS: We have presented a novel, general segmentation algorithm with the advantages of both statistical classifiers and label fusion techniques.


Registration Error Training Point Template Image Segmentation Strategy Registered Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Neil I. Weisenfeld
    • 1
  • Simon K. Warfield
    • 1
  1. 1.Computational Radiology Laboratory, Children’s Hospital BostonHarvard Medical SchoolBostonUSA

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