Radon-Like Features and their Application to Connectomics

Radon-Like Features and their Application to Connectomics

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Radon-Like Features and their Application to Connectomics

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