Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Details

• Alternative Title:
  IEEE Int Symp Technol Homel Security HST
• Personal Author:
  Jurrus, Elizabeth ; Hodas, Nathan ; Baker, Nathan ; Marrinan, Tim ; Hoover, Mark D.
• Description:
  Image classification of nanoparticles from scanning electron microscopes for nuclear forensic analysis is a long, time consuming process. Months of analyst time may initially be required to sift through images in order to categorize morphological characteristics associated with nanoparticle identification. Subsequent assessment of newly acquired images against identified characteristics can be equally time consuming. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web-based canvas framework that partitions images based on features derived from convolutional neural networks. This work is demonstrated using particle images from an aerosol study conducted by Pacific Northwest National Laboratory under the auspices of the U.S. Army Public Health Command to determine depleted uranium aerosol doses and risks.
• Subjects:
  Article
• Source:
  IEEE Int Symp Technol Homel Security HST. 2016.
• Pubmed ID:
  30191203
• Pubmed Central ID:
  PMC6121815
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States
• Volume:
  2016
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:9e24afd883b65032126990791eff0fcd796b6e143104387ce40fd8db1491da30
• Download URL:
  https://stacks.cdc.gov/view/cdc/58858/cdc_58858_DS1.pdf
• File Type:
  [PDF - 601.84 KB ]