<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelghani Tafsat</style></author><author><style face="normal" font="default" size="100%">Mohamed Laid Hadjili</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in 18F-FDG-PET images</style></title><secondary-title><style face="normal" font="default" size="100%">IET Image ProcessingIET Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">389-396</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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&amp;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.</style></abstract></record></records></xml>