Document Detail

Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation.
MedLine Citation:
PMID:  17354845     Owner:  NLM     Status:  MEDLINE    
We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.
Jason J Corso; Eitan Sharon; Alan Yuille
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention     Volume:  9     ISSN:  -     ISO Abbreviation:  Med Image Comput Comput Assist Interv     Publication Date:  2006  
Date Detail:
Created Date:  2007-03-14     Completed Date:  2007-04-06     Revised Date:  2009-12-11    
Medline Journal Info:
Nlm Unique ID:  101249582     Medline TA:  Med Image Comput Comput Assist Interv     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  790-8     Citation Subset:  IM    
Medical Imaging Informatics, University of California, Los Angeles, CA, USA.
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MeSH Terms
Artificial Intelligence*
Bayes Theorem
Brain Neoplasms / pathology*
Computer Simulation
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods
Magnetic Resonance Imaging / methods*
Models, Biological
Models, Statistical
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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