Machine learning of hierarchical clustering to segment 2D and 3D images.
Ontology highlight
ABSTRACT:
SUBMITTER: Nunez-Iglesias J
PROVIDER: S-EPMC3748125 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Machine learning of hierarchical clustering to segment 2D and 3D images.
Nunez-Iglesias Juan J Kennedy Ryan R Parag Toufiq T Shi Jianbo J Chklovskii Dmitri B DB
PloS one 20130820 8
We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron micr ...[more]
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