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Spectral Label Fusion

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2012
Publication Date:
Oct-2012
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
15
Issue Number:
Pt 3
Pages:
410-7
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):410-7.
PubMed ID:
23286157
PMCID:
PMC3539206
Keywords:
Projects:CardiacAblation, Projects:NonparametricSegmentation
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Golland P. Spectral Label Fusion. Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):410-7. PMID: 23286157. PMCID: PMC3539206.
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We present a new segmentation approach that combines the strengths of label fusion and spectral clustering. The result is an atlas-based segmentation method guided by contour and texture cues in the test image. This offers advantages for datasets with high variability, making the segmentation less prone to registration errors. We achieve the integration by letting the weights of the graph Laplacian depend on image data, as well as atlas-based label priors. The extracted contours are converted to regions, arranged in a hierarchy depending on the strength of the separating boundary. Finally, we construct the segmentation by a region-wise, instead of voxel-wise, voting, increasing the robustness. Our experiments on cardiac MRI show a clear improvement over majority voting and intensity-weighted label fusion.

Additional Material
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Wachinger-MICCAI2012-fig4.jpg (123.368kB)