AUTOMATED CROPPING AND ARTIFACT REMOVAL FOR KNIFE-EDGE SCANNING MICROSCOPY
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Knife Edge Scanning Microscopy (KESM) is a high-throughput imaging technique used to obtain large-scale anatomical information (1cm3) at sub-micrometer resolution. Data acquisition has been fully automated, however significant post-processing and reconstruction must be done manually. KESM is unique in that illumination and tissue sectioning are performed using a diamond knife. Therefore many of the physical forces applied to the knife (e.g., vibration, slip, and light refraction) manifest as image artifacts and must be removed in post-processing. In this paper, we propose a fully automated framework to extract valid data from imaged sections and remove lighting artifacts, allowing reconstruction of the volumetric structures in multiple terabyte-scale data sets. © 2011 IEEE.
author list (cited authors)
Kwon, J., Mayerich, D., & Choe, Y.