Picture water droplets

Developing mathematical theories of the physical world: Open Access research on fluid dynamics from Strathclyde

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Mathematics & Statistics, where continuum mechanics and industrial mathematics is a specialism. Such research seeks to understand fluid dynamics, among many other related areas such as liquid crystals and droplet evaporation.

The Department of Mathematics & Statistics also demonstrates expertise in population modelling & epidemiology, stochastic analysis, applied analysis and scientific computing. Access world leading mathematical and statistical Open Access research!

Explore all Strathclyde Open Access research...

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

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

[img]
Preview
Text (Doshi-etal-BSPC2017-Automatic-pharynx-and-larynx-cancer-segmentation-framework)
Doshi_etal_BSPC2017_Automatic_pharynx_and_larynx_cancer_segmentation_framework.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (725kB) | Preview

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 2.2 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.