HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

Anders Boesen Lindbo Larsen, Jacob Schack Vestergaard, Rasmus Larsen

Research output: Contribution to journalJournal articleResearchpeer-review


We present a new method for automatic classification of indirect immunoflourescence images of HEp-2 cells into different staining pattern classes. Our method is based on a new texture measure called shape index histograms that captures second-order image structure at multiple scales. Moreover, we introduce a spatial decomposition scheme which is radially symmetric and suitable for cell images. The spatial decomposition is performed using donut-shaped pooling regions of varying sizes when gathering histogram contributions. We evaluate our method using both the ICIP 2013 and the ICPR 2012 competition datasets. Our results show that shape index histograms are superior to other popular texture descriptors for HEp-2 cell classification. Moreover, when comparing to other automated systems for HEp-2 cell classification we show that shape index histograms are very competitive; especially considering the relatively low complexity of the method.
Original languageEnglish
JournalI E E E Transactions on Medical Imaging
Issue number7
Pages (from-to)1573-1580
Number of pages8
Publication statusPublished - 2014


  • Bioengineering
  • Computing and Processing
  • Accuracy
  • Cell classification
  • Feature extraction
  • feature histograms
  • Histograms
  • Indexes
  • indirect immunofluorescence
  • Shape
  • shape index
  • Shape measurement
  • spatial pooling
  • texture description
  • Vectors


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