@article {14387,
	title = {Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in 18F-FDG-PET images},
	journal = {IET Image Processing},
	volume = {11},
	number = {6},
	year = {2017},
	pages = {389-396},
	abstract = {In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-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\&nbsp;18F-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.},
	url = {http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1024},
	author = {Abdelghani Tafsat and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
