Local structure prediction for gland segmentation

Siyamalan Manivannan, Wenqi Li, Shazia Akbar, Jianguo Zhang, Emanuele Trucco, Stephen J. McKenna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
453 Downloads (Pure)


We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier.
Original languageEnglish
Title of host publication2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
Number of pages4
ISBN (Electronic)9781479923496
ISBN (Print)9781479923502
Publication statusPublished - 16 Jun 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging - Clarion Congress Hotel, Prague, Czech Republic
Duration: 13 Apr 201616 Apr 2016
http://biomedicalimaging.org/2016/ (Link to Conference website)


Conference2016 IEEE 13th International Symposium on Biomedical Imaging
Abbreviated titleISBI 2016
Country/TerritoryCzech Republic
Internet address


  • Glands
  • Image segmentation
  • Feature extraction
  • Support vector machines
  • Training
  • Colon
  • Image
  • analysis


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