Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973609 | Biomedical Signal Processing and Control | 2017 | 11 Pages |
Abstract
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 2.2Â 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.
Keywords
DSCSUSANModified Hausdorff distanceIIHROIPCCMHDRTPCOVMagnetic Resonance Imaging (MRI)BOTRadiotherapy treatment planningAutomatic segmentationRadiotherapyRadiation oncologistLevel set methodLSMHead and neck cancerDice similarity coefficientCoefficient of VariationPearson correlation coefficientFuzzy C-means clusteringfuzzy rulesMean Shiftregion of interestIntensity inhomogeneitybase of tongue
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Trushali Doshi, John Soraghan, Lykourgos Petropoulakis, Gaetano Di Caterina, Derek Grose, Kenneth MacKenzie, Christina Wilson,