Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/11951
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dc.contributor.authorMilankovic I.-
dc.contributor.authorPeulic A.-
dc.contributor.authorYsasi, Alexandra-
dc.contributor.authorWagner W.-
dc.contributor.authorPabst A.-
dc.contributor.authorAckermann M.-
dc.contributor.authorHoudek J.-
dc.contributor.authorFöhst S.-
dc.contributor.authorMentzer S.-
dc.contributor.authorKonerding M.-
dc.contributor.authorFilipovic, Nenad-
dc.contributor.authorTSUDA A.-
dc.date.accessioned2021-04-20T19:38:32Z-
dc.date.available2021-04-20T19:38:32Z-
dc.date.issued2015-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/11951-
dc.description.abstract© 2015 IEEE. Image filtering is one of the most common and important tasks in image processing applications. In this paper, image processing using a mean filtering algorithm combined with thresholding and binarization algorithms for the 3D visualization and analysis of murine lungs is explained. These algorithms are then mapped on the Maxler's MAX2336B Dataflow Engine (DFE) to significantly increase calculation speed. Several different DFE configurations were tested and each yielded different performance characteristics. Optimal algorithm calculation speed was up to 30 fold baseline calculation speed.-
dc.rightsrestrictedAccess-
dc.source2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015-
dc.titleAcceleration of image filtering algorithms for 3D visualization of murine lungs using dataflow engines-
dc.typeconferenceObject-
dc.identifier.doi10.1109/BIBE.2015.7367663-
dc.identifier.scopus2-s2.0-84962865415-
Appears in Collections:Faculty of Engineering, Kragujevac

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