Automatic 3D segmentation of MRI data for detection of head and neck cancerous lymph nodes

Baixiang Zhao, John Soraghan, Gaetano Di Caterina, Lykourgos Petropoulakis, Derek Grose, Trushali Doshi

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

1 Citation (Scopus)
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A novel algorithm for automatic 3D segmentation of magnetic resonance imaging (MRI) data for detection of head and neck cancerous lymph nodes (LN)) is presented in this paper. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. A modified Fuzzy c-mean process is performed through all slices, followed by a probability map which refines the clustering results, to detect the approximate position of cancerous lymph nodes. Fourier interpolation is applied to create an isotropic 3D MRI volume. A new 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on synthetic and real MRI data. The results show that the novel cancerous lymph nodes 3D volume extraction algorithm has over 0.9 Dice similarity score on synthetic data and 0.7 on real MRI data. The F-measure is 0.92 on synthetic data and 0.75 on real data.
Original languageEnglish
Title of host publication2018 Signal Processing
Subtitle of host publicationAlgorithms, Architectures, Arrangements, and Applications (SPA)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)9788362065332
ISBN (Print)9788362065318
Publication statusPublished - 6 Dec 2018
EventSignal Processing Algorithms, Architectures, Arrangements and Applications (SPA) - Poznan, Poland
Duration: 19 Sep 201821 Sep 2018
Conference number: 22


ConferenceSignal Processing Algorithms, Architectures, Arrangements and Applications (SPA)
Internet address


  • MRI data
  • head and neck cancer
  • modified fuzzy c-mean
  • probability map
  • 3D level set method

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