Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images

Trushali Doshi, John Soraghan, Lykourgos Petropoulakis, Gaetano Di Caterina, Derek Grose, Kenneth Mackenzie, Christina Wilson

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
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A novel and effective pharynx and larynx cancer segmentation framework (PLCSF) is presented for automatic base of tongue and larynx cancer segmentation from gadolinium-enhanced T1-weighted magnetic resonance images (MRI). The aim of the proposed PLCSF is to assist clinicians in radiotherapy treatment planning. The initial processing of MRI data in PLCSF includes cropping of region of interest; reduction of artefacts and detection of the throat region for the location prior. Further, modified fuzzy c-means clustering is developed to robustly separate candidate cancer pixels from other tissue types. In addition, region-based level set method is evolved to ensure spatial smoothness for the final segmentation boundary after noise removal using non-linear and morphological filtering. Validation study of PLCSF on 102 axial MRI slices demonstrate mean dice similarity coefficient of 0.79 and mean modified Hausdorff distance of 2.2 mm when compared with manual segmentations. Comparison of PLCSF with other algorithms validates the robustness of the PLCSF. Inter- and intra-variability calculations from manual segmentations suggest that PLCSF can help to reduce the human subjectivity.
Original languageEnglish
Pages (from-to)178-188
Number of pages11
JournalBiomedical Signal Processing and Control
Early online date12 Dec 2016
Publication statusPublished - 1 Mar 2017


  • head and neck cancer
  • automatic segmentation
  • magnetic resonance imaging
  • fuzzy c-means clustering
  • fuzzy rules
  • level set method
  • radiotherapy
  • pharynx
  • larynx
  • tongue
  • pharynx and larynx cancer segmentation framework


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