Open AccessUnsupervised cluster‐based method for segmenting biological tumour volume of laryngeal tumours in 18 F‐FDG‐PET images
Author(s) -
Tafsast Abdelghani,
Hadjili Mohamed Laid,
Bouakaz Ayache,
Benoudjit Nabil
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.1024
Subject(s) - positron emission tomography , cluster analysis , nuclear medicine , computer science , pet ct , volume (thermodynamics) , fluorodeoxyglucose , artificial intelligence , segmentation , pattern recognition (psychology) , radiology , medicine , physics , quantum mechanics
In radiotherapy using 18‐fluorodeoxyglucose positron emission tomography ( 18 F‐FDG‐PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18 F‐FDG‐PET images. The technique is based on the k‐means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c‐means with (35.58%), gradient‐based method with (19.14%) and threshold‐based methods.