Doshi, Trushali and Soraghan, John and Petropoulakis, Lykourgos and Di Caterina, Gaetano and Grose, Derek and Mackenzie, Kenneth and Wilson, Christina (2017) Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images. Biomedical Signal Processing and Control, 33. pp. 178-188. ISSN 1746-8094
Doshi_etal_BSPC2017_Automatic_pharynx_and_larynx_cancer_segmentation_framework.pdf - Accepted Author Manuscript
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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.
|Keywords:||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, Neoplasms. Tumors. Oncology (including Cancer), Health Informatics, Signal Processing, Oncology|
|Subjects:||Medicine > Internal medicine > Neoplasms. Tumors. Oncology (including Cancer)|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
|Depositing user:||Pure Administrator|
|Date Deposited:||05 Jan 2017 10:29|
|Last modified:||23 Apr 2017 01:40|