Recent advances in segmentation methods for connectomics and biomedical imaging produce very large datasets with labels that assign object classes to image pixels. The resulting label volumes are bigger than the raw image data and need compression for efficient storage and transfer. General-purpose compression methods are less effective because the label data consists of large low-frequency regions with structured boundaries unlike natural image data. We present Compresso, a new compression scheme for label data that outperforms existing approaches by using a sliding window to exploit redundancy across border regions in 2D and 3D. We compare our method to existing compression schemes and provide a detailed evaluation on eleven biomedical and image segmentation datasets. Our method provides a factor of 600–2200x compression for label volumes, with running times suitable for practice.
|Original language||English (US)|
|Title of host publication||Medical Image Computing and Computer Assisted Intervention − MICCAI 2017|
|Number of pages||8|
|State||Published - Sep 4 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CCF-2533-01
Acknowledgements: M. Mitzenmacher is supported in part by NSF grants CNS-1228598, CCF-1320231, CCF-1535795, and CCF-1563710. H. Pfister is supported in part by NSF grants IIS-1447344 and IIS-1607800, by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC00002, and by the King Abdullah University of Science and Technology (KAUST) under Award No. OSR-2015-CCF-2533-01.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.