Machine learning of hierarchical clustering to segment 2D and 3D images.
AuthorNunez-Iglesias, J; Kennedy, R; Parag, T; Shi, J; Chklovskii, DB
Source TitlePLoS One
PublisherPublic Library of Science (PLoS)
University of Melbourne Author/sNunez-Iglesias, Juan
AffiliationMedicine Dentistry & Health Sciences
Document TypeJournal Article
CitationsNunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J. & Chklovskii, D. B. (2013). Machine learning of hierarchical clustering to segment 2D and 3D images.. PLoS One, 8 (8), pp.e71715-. https://doi.org/10.1371/journal.pone.0071715.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748125
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 microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.
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