Journal
MEDICAL IMAGE ANALYSIS
Volume 8, Issue 3, Pages 267-274Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2004.06.006
Keywords
medical image; segmentation; statistics; partial differential equation; surface evolution; validation
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Funding
- NCI NIH HHS [P01 CA067165, P01 CA67165] Funding Source: Medline
- NCRR NIH HHS [P41 RR013218, P41RR13218] Funding Source: Medline
- NIBIB NIH HHS [R01EB000304] Funding Source: Medline
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In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise. (C) 2004 Elsevier B.V. All rights reserved.
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