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Dragonfly as a flexible platform for interpreting and processing hyperspectral and other high-dimensional images

Nicolas Piché (1), Francis Côté (1), Eric Yen (1), and Mike Marsh (2)

Microscopy and Microanalysis, 24(S1), 01 August 2018:560-561. DOI: 10.1017/S143192761800329X


2D; 3D; 4D; Dragonfly; Image segmentation; Image visualization; Image analysis


High-dimensional imaging experiments produce results that are data-rich but frustratingly challenging to interpret. Those challenges arise, in large part, due to the lack of easy software tools to display and properly analyze any dataset whose domain spans more than four dimensions. We describe here our extensions to the Dragonfly platform to make the visualization and quantitative analysis of high-dimensionality images user-friendly and accessible for non-experts. Ordinary imaging records a single signal at spatially discrete positions and encodes that digitized signal as a scalar value at every position on a regularly sampled spatial array. Here, we use the terms high-dimensional image, spectrum image, and hyperspectral image interchangeably to describe imaging experiments where the spatial array may be 2D, 3D, or 4D (3D-space plus time), but where instead of capturing a single scalar value at every spatial position, a broad signal array is recorded. That signal array can be an energy spectrum as in the cases of energy electron loss spectroscopy (EELS) or energy dispersive x-ray spectroscopy (EDS)…

Author Affiliation

(1) Object Research Systems, Montreal, Canada
(2) Object Research Systems, Denver, CO, USA

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