Segmentation of organs in pig offal using auto-context

Telmo Amaral, Ilias Kyriazakis, Stephen McKenna, Thomas Plotz

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

3 Citations (Scopus)
393 Downloads (Pure)


The segmentation of 2D images of 3D non-rigid objects into their constituent parts can pose challenging problems, such as missing and occluded parts, weak constraints over the spatial arrangement of parts, and variance in form and appearance. These problems have been addressed via segmentation methods that incorporate spatial context information, such as the auto-context technique. In this paper, we address for the first time the problem of segmenting multiple organs in images of pig offal, a challenging image analysis task that constitutes an essential step towards automated screening at abattoir for signs of sub-clinical diseases. We applied auto-context segmentation to a large data set of images and explored the effect of complementing conventional context features with integral features suited to our application.
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 pages5
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 (Link to Conference website)


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


  • Context
  • Feature extraction
  • Training
  • Image segmentation
  • Heart
  • Lungs
  • Image color analysis


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