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HEp-2 Staining Pattern Classification

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Author

  • Petter Strandmark
  • Johannes Ulén
  • Fredrik Kahl

Summary, in English

Classifying images of HEp-2 cells from indirect immunofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The key to our result is due to carefully designed feature descriptors for multiple level sets of the image intensity. These features characterize both the appearance and the shape of the cell image in a robust manner.

Publishing year

2012

Language

English

Publication/Series

Pattern Recognition (ICPR), 2012 21st International Conference on

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Vision and Robotics (Autonomous Systems)
  • Mathematics

Conference name

21st International Conference on Pattern Recognition (ICPR 2012)

Conference date

2012-11-11 - 2012-11-15

Conference place

Tsukuba, Japan

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISBN: 978-1-4673-2216-4