Radon-Like Features and their Application to Connectomics

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Radon-Like Features and their Application to Connectomics

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dc.contributor.author Pfister, Hanspeter
dc.contributor.author Kumar, Ritwik
dc.contributor.author Vázquez-Reina, Amelio
dc.date.accessioned 2011-08-22T13:26:27Z
dc.date.issued 2011-08-22
dc.identifier.citation Ritwik, Kumar, Amelio Vázquez-Reina, and Hanspeter Pfister. 2010. Radon-like features and their application to connectomics. Paper presented at the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA10), San Francisco, CA, June 15-17, 2010. en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:5112791
dc.description.abstract In this paper we present a novel class of so-called Radon-Like features, which allow for aggregation of spatially distributed image statistics into compact feature descriptors. Radon-Like features, which can be efficiently computed, lend themselves for use with both supervised and unsupervised learning methods. Here we describe various instantiations of these features and demonstrate there usefulness in context of neural connectivity analysis, i.e. Connectomics, in electron micrographs. Through various experiments on simulated as well as real data we establish the efficacy of the proposed features in various tasks like cell membrane enhancement, mitochondria segmentation, cell background segmentation, and vesicle cluster detection as compared to various other state-of-the-art techniques. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dash.license LAA
dc.title Radon-Like Features and their Application to Connectomics en_US
dc.type Conference Paper en_US
dc.description.version Author's Original en_US
dash.depositing.author Pfister, Hanspeter
dc.date.available 2011-08-22T13:26:27Z
dash.affiliation.other Initiative in Innovative Computing en_US

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  • FAS Scholarly Articles [7594]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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