Semi-automated segmentation of pollen grains in microscopic images: A tool for three imaging modes

Stefan Johnsrud, Huiguang Yang, Ashwin Nayak, Surangi Waduge Punyasena

Research output: Contribution to journalArticlepeer-review


Image analysis methods have the potential to increase the accuracy and rates of data collection in palynological research. Automated segmentation of pollen grains is a method that would facilitate image-based palynological analysis by creating large reference image libraries. We developed an executable for the automated segmentation and cropping of pollen grains from microscope images based on pixel intensity values. Our method works with images taken using transmitted-light, widefield-fluorescence, structured illumination (Apotome), and includes a novel approach for cropping the Apotome Z-stack. The system crops pollen grains from sampled fields of view with ~97% recall and ~97% precision for transmitted-light and widefield-fluorescence images, and ~90% recall and ~89% precision for Apotome fluorescence images. Results differed between different imaging wavelengths for fluorescence images, with Apotome images showing the greatest difference between red and green emission wavelengths. Recall in cropping of transmitted-light images was comparable to previous segmentation efforts.

Original languageEnglish (US)
Pages (from-to)181-191
Number of pages11
Issue number3
StatePublished - 2013


  • Apotome
  • computer vision
  • cropping
  • fluorescence microscopy
  • image analysis
  • image processing
  • segmentation
  • transmitted-light

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Plant Science


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