Molecular imaging using fluorescence microendoscopy offers promise for the optical biopsy of cancer to inform precision medicine. However, microscopic resolution generally comes with the trade-off of a tiny field of view and tunnel vision. Micro-image mosaicking offers the capability of stitching together larger scenes of the tissue to aid visualization and interpretation. The development of hyperspectral microendoscopes provides motivation for adapting mosaicking algorithms to process a plurality of simultaneous channels. We present an algorithm that mosaics hyperspectral microendoscopic video by correlating channels of consecutive frames as a basis for calculating image alignments. A typical raster path to produce suitable data for mosaicking images the same location several times redundantly in different frames, making this algorithm well-suited for analyzing video-rate data. To complement this data rate, we employ parallel processing via GPUs to alleviate computational bottlenecks and approach video-rate mosaicking speeds. This implementation lays the foundation for real-time multi-channel mosaicking to accompany video-rate hyperspectral microendoscopic probes.
Ryan Lang, Jacob Tatz, Eric Kercher, Dana Brooks, and Bryan Spring, "Micro-image mosaicking for video-rate multi-channel fluorescence microendoscopy (Conference Presentation)," Proc. SPIE 10854, Endoscopic Microscopy XIV, 1085413 (Presented at SPIE BiOS: February 03, 2019; Published: 4 March 2019); https://doi.org/10.1117/12.2510880.6008521639001.
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