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Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain

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3 Author(s)
Cappabianco, F.A.M. ; Inst. of Comput., State Univ. of Campinas, Campinas ; Falcao, A.X. ; Rocha, L.M.

A new approach to identify clusters as trees of an optimum- path forest has been presented. We are extending the method for large datasets with application to automatic GM/WM classification in MR-T1 images of the brain. The method is computed for a few randomly selected voxels, such that GM and WM define two optimum-path trees. The remaining voxels are classified incrementally, by identifying which tree would contain each voxel if it were part of the forest. Our method produces accurate results on phantom and real images, similarly to those obtained by the state-of-the-art, does not rely on templates, and takes less than 1.5 minute on modern PCs.

Published in:

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Date of Conference:

14-17 May 2008