Modified fuzzy c-means clustering for automatic tongue base tumour extraction from MRI data

Trushali Doshi, John Soraghan, Derek Grose, Kenneth MacKenzie, Lykourgos Petropoulakis

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

7 Citations (Scopus)
105 Downloads (Pure)

Abstract

Magnetic resonance imaging (MRI) is a widely used imaging modality to extract tumour regions to assist in radiotherapy and surgery planning. Extraction of a tongue base tumour from MRI is challenging due to variability in its shape, size, intensities and fuzzy boundaries. This paper presents a new automatic algorithm that is shown to be able to extract tongue base tumour from gadolinium-enhanced T1-weighted (T1+Gd) MRI slices. In this algorithm, knowledge of tumour location is added to the objective function of standard fuzzy c-means (FCM) to extract the tumour region. Experimental results on 9 real MRI slices demonstrate that there is good agreement between manual and automatic extraction results with dice similarity coefficient (DSC) of 0.77±0.08.
Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages2460 - 2464
Number of pages5
ISBN (Print)9780992862619
Publication statusPublished - 5 Sep 2014
Event22nd European Signal Processing Conference - Lisbon Congress Centre, Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014
Conference number: 2014

Conference

Conference22nd European Signal Processing Conference
Abbreviated titleEUSIPCO
Country/TerritoryPortugal
CityLisbon
Period1/09/145/09/14

Keywords

  • Hessian analysis
  • MRI
  • automatic tumour extraction
  • fuzzy c-means
  • throat detection
  • magnetic resonance imaging
  • image segmentation
  • clustering algorithms

Fingerprint

Dive into the research topics of 'Modified fuzzy c-means clustering for automatic tongue base tumour extraction from MRI data'. Together they form a unique fingerprint.

Cite this